Geographische Daten, wie etwa Bewegungsdaten oder geolokalisierte Messungen über die Zeit, sind oft mehrere Gigabyte groß und können daher nicht mehr mit klassischen online Tools analysiert und präsentiert werden. Wir wollen helfen, Datenwissenschaftler_innen, Datenjournalist_innen und der breiten Masse diese Daten durch interaktive Echtzeitvisualisierung im Web zugänglich machen.
Geographische Daten, wie etwa Bewegungsdaten oder geolokalisierte Messungen über die Zeit, sind oft mehrere Gigabyte groß und können daher nicht mehr mit klassischen online Tools analysiert und präsentiert werden. Wir wollen helfen, Datenwissenschaftler_innen, Datenjournalist_innen und der breiten Masse diese Daten durch interaktive Echtzeitvisualisierung im Web zugänglich machen.
Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "Multimodal representations", "Vision-language models", "Time modeling", "Time estimation", "Benchmark dataset" ], "weblinks": [ { "href": "https://tekayanidham.github.io/timeline-page/", "caption": null, "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1536, "image_height": 458, "name": "tekaya-2025-amo-teaser.png", "type": "image/png", "size": 4646052, "path": "Publication:tekaya-2025-amo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/tekaya-2025-amo/tekaya-2025-amo-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "d9522", "workgroup_id": "vis", "drupal_id": 9522, "drupal_path": "/research/projects/VaCoViCu2", "name": "Visual Analytics and Computer Vision meet Cultural Heritage", "name_de": "Visuelle Analytik und Computer Vision treffen auf kulturelles Erbe", "short_title": "VaCoViCu2", "website": "http://www.visual-heritage.at", "status": "active", "short_abstract": null, "abstract": "Wider research context/theoretical framework
\nIn the last decades, archives and museums made extensive efforts to foster the digital preservation of cultural heritage (CH) artifacts such as historical photographs and amateur films. These collections of digitized artifacts offer immense potential to increase the knowledge of our heritage by investigating patterns and relationships. The systematic analysis and presentation of these large amounts of visual media data are, however, still strongly limited due to the lack of metadata and suitable analysis approaches. This impedes historians and film archivists but also lay users in analyzing, interpreting, and thereby preserving the memories of human cultural history.
\nHypotheses/research questions/objectives
\nOur cooperative doctoral programme aims to close basic and applied research gaps by combining computer vision (CV) and visual analytics (VA). We jointly work on the advancement of interactive analysis, exploration, and presentation of historical visual media collections. The central hypothesis behind this is that combining CV and VA is necessary to unlock large cultural heritage collections and open them to a wide range of users.
\nApproach/methods
\nWe follow a problem-driven research paradigm determined by the cultural heritage domain. We leverage human-in-the-loop machine learning (ML) and integrate CV and VA methods for the analysis of cultural objects and their relationships across space and time. This includes ML, information retrieval and CV for analysis as well as situated visualization and storytelling for exploration and presentation. We quantitatively evaluate the results against ground truth annotations and through user studies.
\nFaculty
\nThe project is a collaboration of researchers from St. Pölten University of Applied Sciences (FHSTP) and TU Wien. Seven faculty members contribute complementary research expertise on CV, VA, and CH across basic and applied research. The project shall elevate the existing scholarly ties between FHSTP and TU Wien to a new level of inter-institutional cooperation.
\nDoctoral programme
\nThe doctoral programme foresees 5 thematically related PhDs. The topics focus on the interactive analysis of visual media in cultural heritage collections and complement each other in terms of methods, use cases, and addressed users. Each topic is supervised by a team of 2-3 researchers (at least one from each institution), and a comprehensive education and training programme tailored to research is provided.
\nAdded value
\nAdded value is generated at multiple levels: 1) embedding doctoral students in a team of high profile visual computing researchers; 2) increasing junior faculty members’ experience in supervision skills; 3) leveraging the complementary strengths of applied and basic research; 4) sharing of resources between institutions; 5) opening up large cultural heritage collections to a wide range of users by combining CV and VA.
", "start_date": "2023-10-01", "end_date": "2027-09-30", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:d9522", "type": "image/png", "size": 17914, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/d9522/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [], "research_areas": [ "InfoVis" ], "publications": [ "aigner-2025-vhv", "tekaya-2025-amo" ], "url": "https://www.cg.tuwien.ac.at/research/projects/VaCoViCu2", "__class": "Project" }, { "id": "vis", "drupal_id": 1354, "name": "Visualization Group", "website": "https://www.cg.tuwien.ac.at/group/Visualization-Group", "projects": [ "ADAPT", "AngioVis", "Animated Cell Tab development", "AutARG", "avr", "BioNetIllustration", "COMRADE", "d10027", "d10162", "d10164", "d10206", "d10214", "d10413", "d10450", "d10489", "d4328", "d4335", "d4534", "d9275", "d9282", "d9522", "d9555", "d9671", "d9951", "deskollage", "diagvis", "exvisation", "Illustrare", "illvisation", "KASI", "ManyViews", "NeuroViewer", "ProcessIllVis", "ProKeyAnim", "PVG", "Scale-VS", "Scenario Pool", "Semantic Steering", "Sim-CT", "Smart-CT", "Smile Analytics", "ViMaL", "VISAR" ], "research_areas": [ "BioVis", "IllVis", "InfoVis", "MedVis", "NetVis", "Perception", "VisMat", "VR" ], "publications": [ "2013_Viola_Ivan_2013_MTS", "2015-image-coll", "2017-kouril-illcell", "2019-ic", "AA2020", "aigner-2025-vhv", "alacruzECR2004", "alacruzVisSym2004", "Alharbi_2021", "Alsallakh_2012_RCW", "alsallakh-2009-iva", "altendorfer-2008-vts", "amabili2021", "amirkhanov_2012", "amirkhanov_2012heur", "amirkhanov-2011", "amirkhanov-2014-ama", "amirkhanov-2018-withteeth", "amirkhanov-2021-diss", "amirkhanov2010AMA", "ammer-2008-lst", "An-2018-CHI", "AndreUrsits_2013_APP", "antes-2023-vhv", "Antonini2020", "arbesser_ba_2023", "Artner-2004-Hig", "Artner-2005-Hig", "Artner-2005-Thesis", "audioflow", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2007-ant", "balabanian-2008-hvv", "Balabanian-2010-IIV", "Balabanian-2010-PSDSV", "Balduz_01", "Basch_2011_ATA", "Bauer_David_2018", "Bauer_Johannes_2014_IWB", "bauer-2015-cbfd", "Bayat_2019", "bayer-2015-gapaot", "bbeer-2013", "Bechtold2020", "Beer_2011_DVE", "Beham_Michael_PSC", "beham-2014-cupid", "bernhard-2006-dvrcta", "bernhard-2014-GTOM", "bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", 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"knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "Modeling", "drupal_id": 4130, "name": "Modeling", "short_abstract": "In this area, we focus on researching methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "abstract": "One of the major bottlenecks in the field of computer graphics is the one of content creation, particularly the creation of high-quality 3D geometric models. These models are a demand in many industries, such as entertainment, computer-aided design, urban planning, the fabrication of physical objects, as well as research areas like medical or scientific visualizations and simulations.\n\nWhile rendering - the classical field of computer graphics - has reached very high standards in the recent two decades, the issue of the creation of appropriate digital models is a difficult problem that hardly keeps pace with current demands. Indeed, it is a complex creative process that involves both artistic and technical skills.\n\nTo make this process easier, our goal in this research area is to find new methods and algorithms that facilitate creation, representation, analysis and processing of 3D models.", "website": "https://www.cg.tuwien.ac.at/research/area/Modeling", "teaser": { "name": "teaser.jpg", "path": "research_area:Modeling", "type": "image/jpeg", "size": 190581, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Modeling/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend" ], "publications": [ "AA2020", "Adorjan-2015", "aichner-2016-sadf", "arikan-2013-osn", "birsak-thesis", "Birsak2018-SA", "Brandstaetter2020", "brunner_lukas_2022_ssb", "Caic2021", "clemenz_2019_rosy_fields", "DOG10", "dusberger-2010-msm", "eibens-2021-tsgpmg", "eibensteiner-2021-pmcg", "eibner-2009-gpc", "Erhardt_2025_Scutes", "erler_2024_ppsurf", "erler_philipp-2016-femfluid-prakt", "erler_philipp-2017-phd", "erler-2020-p2s", "erler-2025-lidarscout", "fan-2014-scfl", "ferschin-1992-kons", "fiedler-2009-php", "fleiss-2015-da", "GAHR-2013-AGM", "gamsjaeger-2022-proc", "gersthofer-2016-sosob", "gervautz-1989-sol1", "gervautz-1993-use", "griessmair-1989-def1", "groeller-1992-frac", "groeller-1995-mod", "Groeller-1995-SDC", "Grossfurtner-2023-paego", "Guerrero-2014-GRF", "Guerrero-2014-TPS", "guerrero-2015-lsp", "HECHER-2017-HDY", "hiess-1992-comb", "hofer_kerstin-2023-sketch2model", "honic-2020-w78", "HU-2017-ETM", "HUMMEL-2001-MSE", "Ilcik_2015_LAY", "ilcik-2010-ps", "ilcik-2013-cipmi", "ilcik-2014-cgbpmi", "ilcik-2016-cmssg", "ilcik-2021-xmas", "kapferer_2021_advancing", "karnik-09-shapegrammar", "Kellner-2021-DA", "kendlbacher-2016", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koenigsberger-2024-msu", "komon-2025-webigeo", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kraehsmaier-dsc", "kreuzer-2016-isf", "kuehtreiber-2010-ikph", "leimer_2017_rbpesc", "leimer_2020-cag", "leimer-2016-coan", "leimer-2016-rpe", "leimer-2018-sar", "leimer-2023-ecd", "LEOPOLD-2017-ALG", "LIPP-2007-CGA", "LIPP-2008-IEV", "LIPP-2009-PGL", "LIPP-2009-PGL2", "lipp2011a", "macho_jonas-2024-baa", "Maierhofer-thesis", "manpreet_kainth-2012-rus", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_thesis_2019", "mindek-xmas-card-2016", "mujadzic-2023-hfp", "musialski-2009-sbfr", "musialski-2010-pfi", "musialski-2010-tof", "musialski-2012-icb", "musialski-2012-sur", "Musialski-2013-ipmum", "musialski-2013-surcgf", "NIEDERREITER-2010-SIM", "Pahr2020", "Parzer_2013_IIG", "pegrisch_2018_uvs", "pogrzebacz-2014-gggm", "polzer-sog", "preiner_2017_phd", "PUEYO-2019-SCL", "RADAX-2010-LOD", "Radwan_2021_Occ", "rasch-2016-imgses", "recheis-2009-arr", "Reichinger-12", "reisinger-2023-iad", "reisner-2010-ind", "reisner-2011-akgeo", "reitsamer-2008-tsud", "Rumpelnik_Martin-2020-NPR", "scharl-master-thesis", "scholz_2017_bac", "SCHWAERZLER-2017-SBGM", "SIETZEN-2017-BBO", "sifuentes-2018-crowd", "sippl-2014-fss", "sperl-2013-BA", "spitaler-2015-pbi", "steiner_2016_isad", "steiner-2014-da", "steinkellner-2022-dll", "steinschorn-2023-p2m", "steinschorn-2025-par", "steinschorn-2025-parameter", "tobler02_amrmg", "tobler02_mbpls", "Traxler-1996-GAI", "VASILJEVS-2018-PMPL", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "weiner_2011", "WIMMER-2015-CGFC", "WIMMER-2016-FUTURIS", "WIMMER-2020-ASG", "wimmer-2023-acd", "Winkler-2017", "WINKLER-2019-PDG", "Wonka-2003-Ins", "xmas-2024", "zeiller-1993-coll", "Zotti-2003-AMP", "zotti-2003-ash", "zotti-2006-pla", "zotti-2007-eg" ], "projects": [ "3DSpatialization", "d4388", "EVOCATION", "GAMEWORLD", "Harvest4D", "PAMINA", "Photo-Guide", "REPLICATE", "SCI_BIM", "Wohnen 4.0" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Modeling", "__class": "ResearchArea" } ], "keywords": [ "Visualisierung", "Lawinen", "3D Karten" ], "weblinks": [ { "href": "https://webigeo.alpinemaps.org/", "caption": "demo", "description": "weBIGeo demo: requires WebGPU-ready browser! ", "main_file": 1 }, { "href": "https://lawinensymposium.naturfreunde.at/", "caption": "Lawinensymposium", "description": "Lawinensymposium", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "komon-2025-webigeo-paper.pdf", "type": "application/pdf", "size": 1611394, "path": "Publication:komon-2025-webigeo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2556, "image_height": 1303, "name": "komon-2025-webigeo-teaser.png", "type": "image/png", "size": 5204641, "path": "Publication:komon-2025-webigeo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "d9555", "workgroup_id": "vis", "drupal_id": 9555, "drupal_path": "/research/projects/weBIGeo", "name": "Massive geographische Datenvisualisierung mit WebGPU", "name_de": "Massive geographische Datenvisualisierung mit WebGPU", "short_title": "weBIGeo", "website": "https://www.cg.tuwien.ac.at/research/projects/weBIGeo", "status": "active", "short_abstract": "Geographische Daten, wie etwa Bewegungsdaten oder geolokalisierte Messungen über die Zeit, sind oft mehrere Gigabyte groß und können daher nicht mehr mit klassischen online Tools analysiert und präsentiert werden. Wir wollen helfen, Datenwissenschaftler_innen, Datenjournalist_innen und der breiten Masse diese Daten durch interaktive Echtzeitvisualisierung im Web zugänglich machen.
Geographische Daten, wie etwa Bewegungsdaten oder geolokalisierte Messungen über die Zeit, sind oft mehrere Gigabyte groß und können daher nicht mehr mit klassischen online Tools analysiert und präsentiert werden. Wir wollen helfen, Datenwissenschaftler_innen, Datenjournalist_innen und der breiten Masse diese Daten durch interaktive Echtzeitvisualisierung im Web zugänglich machen.
Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "Class-centric labeling", "Interactive machine learning", "Property measures", "Visual analytics", "Visual-interactive data labeling" ], "weblinks": [], "files": [ { "description": "class-centric visual interactive labeling workflow", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1667, "image_height": 624, "name": "matt-2025-scv-teaser.png", "type": "image/png", "size": 254694, "path": "Publication:matt-2025-scv", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/matt-2025-scv/matt-2025-scv-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/matt-2025-scv/matt-2025-scv-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "d9282", "workgroup_id": "vis", "drupal_id": 9282, "drupal_path": "/research/projects/JDE", "name": "Joint Human-Machine Data Exploration", "name_de": "Joint Human-Machine Data Exploration", "short_title": "JDE", "website": "https://www.cg.tuwien.ac.at/research/projects/JDE", "status": "active", "short_abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
", "abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
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We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zero-shot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. 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"name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. 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Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
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We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
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The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
", "abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
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Forschungsforum Der Österreichischen Fachhochschulen", "lecturer": [ { "id": 5543, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Wolfgang", "lastname": "Aigner", "searchname": "Wolfgang Aigner", "is_teacher": false, "name": "Wolfgang Aigner", "publications": [], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "aigner-2025-vhv", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5543", "__class": "Author" } ], "location": "Wien", "pages": "2", "pages_from": "558", "pages_to": "559", "research_areas": [], "keywords": [ "Artificial Intelligence", "Visual Analytics", "Computer Vision", "Cultural Heritage", "Visualization", "Computer Science" ], "weblinks": [], "files": [], "projects_workgroups": [ { "id": "d9522", "workgroup_id": "vis", "drupal_id": 9522, "drupal_path": "/research/projects/VaCoViCu2", "name": "Visual Analytics and Computer Vision meet Cultural Heritage", "name_de": "Visuelle Analytik und Computer Vision treffen auf kulturelles Erbe", "short_title": "VaCoViCu2", "website": "http://www.visual-heritage.at", "status": "active", "short_abstract": null, "abstract": "Wider research context/theoretical framework
\nIn the last decades, archives and museums made extensive efforts to foster the digital preservation of cultural heritage (CH) artifacts such as historical photographs and amateur films. These collections of digitized artifacts offer immense potential to increase the knowledge of our heritage by investigating patterns and relationships. The systematic analysis and presentation of these large amounts of visual media data are, however, still strongly limited due to the lack of metadata and suitable analysis approaches. This impedes historians and film archivists but also lay users in analyzing, interpreting, and thereby preserving the memories of human cultural history.
\nHypotheses/research questions/objectives
\nOur cooperative doctoral programme aims to close basic and applied research gaps by combining computer vision (CV) and visual analytics (VA). We jointly work on the advancement of interactive analysis, exploration, and presentation of historical visual media collections. The central hypothesis behind this is that combining CV and VA is necessary to unlock large cultural heritage collections and open them to a wide range of users.
\nApproach/methods
\nWe follow a problem-driven research paradigm determined by the cultural heritage domain. We leverage human-in-the-loop machine learning (ML) and integrate CV and VA methods for the analysis of cultural objects and their relationships across space and time. This includes ML, information retrieval and CV for analysis as well as situated visualization and storytelling for exploration and presentation. We quantitatively evaluate the results against ground truth annotations and through user studies.
\nFaculty
\nThe project is a collaboration of researchers from St. Pölten University of Applied Sciences (FHSTP) and TU Wien. Seven faculty members contribute complementary research expertise on CV, VA, and CH across basic and applied research. The project shall elevate the existing scholarly ties between FHSTP and TU Wien to a new level of inter-institutional cooperation.
\nDoctoral programme
\nThe doctoral programme foresees 5 thematically related PhDs. The topics focus on the interactive analysis of visual media in cultural heritage collections and complement each other in terms of methods, use cases, and addressed users. Each topic is supervised by a team of 2-3 researchers (at least one from each institution), and a comprehensive education and training programme tailored to research is provided.
\nAdded value
\nAdded value is generated at multiple levels: 1) embedding doctoral students in a team of high profile visual computing researchers; 2) increasing junior faculty members’ experience in supervision skills; 3) leveraging the complementary strengths of applied and basic research; 4) sharing of resources between institutions; 5) opening up large cultural heritage collections to a wide range of users by combining CV and VA.
", "start_date": "2023-10-01", "end_date": "2027-09-30", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:d9522", "type": "image/png", "size": 17914, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/d9522/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [], "research_areas": [ "InfoVis" ], "publications": [ "aigner-2025-vhv", "tekaya-2025-amo" ], "url": "https://www.cg.tuwien.ac.at/research/projects/VaCoViCu2", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/aigner-2025-vhv/", "__class": "Publication" }, { "id": "grammatikaki-2025-htr", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/215590", "title": "How to represent landmark trees in digital 3D maps? An automated workflow and user study", "date": "2025-05", "abstract": "Digital 3D maps created from digital elevation models (DEMs) cannot properly capture trees due to the 2.5D nature of the DEMs. Leveraging publicly available DEMs and orthophotos as the only input data sources, we present a fully automatic pipeline that models landmark trees. We conducted two crowdsourced user studies to evaluate visual appeal and scene recognizability using two different levels of detail of tree representations generated using our pipeline. Users found highly detailed trees much more appealing, and also easier and more trustworthy to recognize, with open-ended responses revealing key factors like realism, coherence, and tree shape influencing their preferences. However, the ability to recognize a location seems to depend more on the surrounding environment than the representation of the landmark tree in focus. We discuss the implications of these results for digital 3D outdoor maps.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2200, "image_height": 370, "name": "grammatikaki-2025-htr-image.jpg", "type": "image/jpeg", "size": 181956, "path": "Publication:grammatikaki-2025-htr", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,pages_from,pages_to,volume", "repositum_presentation_id": null, "authors": [ { "id": 1937, "drupal_id": 5371, "orcid_id": "0000-0001-5779-2182", "website": "https://www.cg.tuwien.ac.at/staff/AngelikiGrammatikaki", "firstname": 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"bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", 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"wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/", "__class": "Publication" }, { "id": "pahr-2024-ieo", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/199161", "title": "Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes", "date": "2024-06", "abstract": "We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed-methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process—mainly through non-visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2634, "image_height": 1232, "name": "pahr-2024-ieo-teaser.png", "type": "image/png", "size": 172079, "path": "Publication:pahr-2024-ieo", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/pahr-2024-ieo/pahr-2024-ieo-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects,date_from,date_to,event,lecturer,pages_from,pages_to", "repositum_presentation_id": null, "authors": [ { "id": 1813, "drupal_id": 2772, "orcid_id": 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"InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "MedVis", "drupal_id": 4133, "name": "Medical Visualization", "short_abstract": "This research area bridges the gap between visualization and medicine by providing comprehensible visual means to detect diseased regions or to aid clinical treatment decisions.", "abstract": "In collaboration with hospitals we try to investigate various topics in medicine by means of visualization or visual analytics. Due to the increasing amount of data generated by hospital information systems, visualization becomes successively more important. Extracting certain trends from such highly diverse data by suitable visual mappings is one of the primary goals of medical visualization. The data is usually acquired by computed tomography, magnetic resonance imaging or ultrasound.\n\nCurrently, our research addresses the analysis of blood vessels such as the inspection of possible stenotic regions or their spatial arrangement as in the Circle of Willis. Furthermore, we investigate aneurysm classification and lesion detection as well as visualization. Another research area deals with realistic prenatal visualization of the fetus in-vivo and in real-time. Due to the high variability of medical imaging data, we further focus on high-level correction of segmentation defects.", "website": "https://www.cg.tuwien.ac.at/research/area/MedVis", "teaser": { "name": "teaser.png", "path": "research_area:MedVis", "type": "image/png", "size": 225330, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/MedVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "amabili2021", "amirkhanov-2018-withteeth", "amirkhanov-2021-diss", "antes-2023-vhv", "arbesser_ba_2023", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2008-hvv", "Balabanian-2010-IIV", "Bauer_David_2018", "bayat-2022-mva", "Bernold_2019", "bernold-2019-epr", "Birkeland-2012-IMC", "borondy2022", "bront-2023-svo", "bruckner-2010-HVC", "burns-2007-fea", "Chen-Information-2016", "djuric-2020-wvo", "Eckelt_2017", "Eichner2022", "EuroVis2018-ShortPapers-Karall", "fmistelbauer-2014-adict", "Ford-2012-HRV", "furmanova_2020", "Gadllah_Hani_2016", "Gall_2017", "gall_alexander_2016BA", "ganglberger2019", "Gietl_2020", "Gillmann2021", "Groeller_2016_P4", "Groeller_V42020", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "gutekunst_2019", "Hainoun2019", "Heim-2017", "Ismail-2018", "jank-2022-cbf", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "Kovacs_PhD", "Machegger2018DTI", "magg-2021-dva", "magg2021", "magg2022", "malan_fluoro", "martorell2022", "Masopust_2018", "Mazza_2020", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2017", "mindek-2017-dsn", "Mistelbauer_2021", "Mistelbauer_Gabriel_2016", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "moerth_eric-2016-3DF", "moerth-2018-tpose", "Musleh_PhD", "musleh-2023-ugi", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "PERNDORFER-2015-ECS", "Pezenka-2016-MT", "Philipp_Hochhauser_2023", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_visgap2020", "raidou2018visualflatter", "Reiter2017CVAP", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "ruiz-2008-OVR", "ruiz-2008-SEV", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "Schmidlehner_Sandra_SbC", "Schrempf_2018", "schultes_lampi", "SifuentesGangl2017FIS", "solteszova-2010-MOS", "Solteszova2016", "Spelitz2020", "stritzel2022", "Stritzel2022_thesis", "Tramberger_2018", "Tramberger2017BCS", "unger_2017", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_HQ3", "Viola_Ivan_DAC", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "vitruvian_2019", "vogel2022", "Weissenboeck_2019_PhD" ], "projects": [ "d9951", "Smile Analytics" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=MedVis", "__class": "ResearchArea" }, { "id": "Perception", "drupal_id": 4136, "name": "Perception", "short_abstract": "In this area, our research focuses on understanding and using the capabilities of human perception for designing high-quality graphics algorithms and effective visualizations.", "abstract": "In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
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Visual Interactive Labeling", "date": "2024-05-27", "abstract": "We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property measures to support instance labeling for labeling difficult instances and batch labeling to quickly label easy instances. Simulated experiments reveal that cVIL with batch labeling can outperform traditional labeling approaches based on active learning. 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"https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1110", "__class": "Author" } ], "booktitle": "Eurographics Proceedings", "date_from": "2024-05-27", "date_to": "2024-05-27", "doi": "10.2312/eurova.20241113", "editor": "El-Assady, Mennatallah and Schulz, Hans-Jorg", "event": "EuroVis Workshop on Visual Analytics (EuroVA 2024)", "isbn": "978-3-03868-056-7", "lecturer": [ { "id": 5263, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Matthias", "lastname": "Matt", "searchname": "Matthias Matt", "is_teacher": false, "name": "Matthias Matt", "publications": [ "matt-2023-vae", "matt-2024-cvi", "matt-2024-cvil", "matt-2025-scv" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "matt-2024-cvil", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5263", "__class": "Author" } ], "location": "Aarhus", "open_access": "yes", "pages": "6", 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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "Visual Analytics", "Interactive Machine Learning", "User Interface Design" ], "weblinks": [ { "href": "https://diglib.eg.org/server/api/core/bitstreams/c18fafcc-b4b4-4e51-bd2f-cec056c6d93a/content", "caption": "paper", "description": null, "main_file": 1 }, { "href": "https://gitlab.tuwien.ac.at/e193-02-jde/lava", "caption": "GitLab", "description": null, "main_file": 1 } ], "files": [ { "description": "Screenshot of cVIL as employed in the user study", "filetitle": "cVIL teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1104, "image_height": 449, "name": "matt-2024-cvil-cVIL teaser.png", "type": "image/png", "size": 293869, "path": "Publication:matt-2024-cvil", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/matt-2024-cvil/matt-2024-cvil-cVIL teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/matt-2024-cvil/matt-2024-cvil-cVIL teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "d9282", "workgroup_id": "vis", "drupal_id": 9282, "drupal_path": "/research/projects/JDE", "name": "Joint Human-Machine Data Exploration", "name_de": "Joint Human-Machine Data Exploration", "short_title": "JDE", "website": "https://www.cg.tuwien.ac.at/research/projects/JDE", "status": "active", "short_abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
", "abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
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In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., “accumulated stripes”) design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes’ design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes’ design outperformed the line charts in accuracy for all study tasks.", "authors_et_al": false, "substitute": null, "main_image": { "description": "AccuStripes - Graphical Abstract", "filetitle": "accuStripes", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 428, "name": "heim-2024-accustripes-accuStripes.png", "type": "image/png", "size": 366335, "path": "Publication:heim-2024-accustripes", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/heim-2024-accustripes-accuStripes:thumb{{size}}.png" }, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ { "id": 1354, "drupal_id": 1133, "orcid_id": null, "website": 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explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", 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"moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", 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"Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", 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"https://www.cg.tuwien.ac.at/research/publications/2024/bayat-2024-awt/", "__class": "Publication" }, { "id": "boffi-2024-bagginghook", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/197699", "title": "BaggingHook: Selecting Moving Targets by Pruning Distractors Away for Intention-Prediction Heuristics in Dense 3D Environments", "date": "2024", "abstract": "Selecting targets in dense, dynamic 3D environments presents a significant challenge. In this study, we introduce two novel selection techniques based on distractor pruning to assist users in selecting targets moving unpredictably: BaggingHook and AutoBaggingHook. Both are built upon the Hook intention-prediction heuristic, which continuously measures the distance between the user's cursor and each object to compute per-object scores and estimate the intended target. Our techniques reduce the number of targets in the environment, making heuristic convergence potentially faster. Once pruned away, distractors are also made semi-transparent to reduce occlusion and the overall difficulty of the task. However, their motion is not altered, so that users can still perceive the dynamics of the environment. We designed two pruning approaches: BaggingHook lets users manually prune distractors away, while AutoBaggingHook uses automated, score-based pruning. We conducted a user study in a virtual reality setting inspired by molecular dynamics simulations, featuring crowded scenes of objects moving fast and unpredictably, in 3D. We compared both proposed techniques to the Hook baseline under more challenging circumstances than it had previously been tested. Our results show that AutoBaggingHook was the fastest, and did not lead to higher error rates. BaggingHook, on the other hand, was preferred by the majority of participants, due to the greater degree of control it provides to users, leading some to see entertainment value in its use. This work shows the potential benefits of varying the types of inputs used in intention-prediction heuristics, not just to improve performance, but also to reduce occlusion, overall task load, and improve user experience.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5365, "drupal_id": null, "orcid_id": "0000-0003-4579-2606", "website": null, "firstname": "Paolo", "lastname": "Boffi", "searchname": "Paolo Boffi", "is_teacher": false, "name": "Paolo Boffi", "publications": [ "boffi-2024-bagginghook" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=5365", "__class": "Author" }, { "id": 5366, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Alexandre", "lastname": "Kouyoumdjian", "searchname": "Alexandre Kouyoumdjian", "is_teacher": false, "name": 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Tourengeher·innen typischerweise vorab den aktuellen Lawinenlagebericht (LLB) sowie die Geländeeigenschaften, wie Hangneigung, Höhe und Exposition der geplanten Tour auf einer Karte. Reduktionsmethoden wie Stop-or-Go oder die SnowCard können sowohl bei der Planung als auch vor Ort angewandt werden, um das Risiko abzuschätzen. Bei korrekter Anwendung dieser Methoden könnte ein Großteil der Todesfälle vermieden werden. Die Anwendung umfasst jedoch mehrere kognitiv aufwändige Schritte: Im ersten Schritt müssen Tourengeher·innen die Informationen aus LLB und Karte korrekt verknüpfen und anhand der gewählten Methode interpretieren, um potenziell kritische Regionen entlang der Route vorab identifizieren zu können. Im zweiten Schritt müssen potenziell kritische Regionen auch während der Tour als solche wiedererkannt und vor Ort beurteilt werden. \nUm die Anwendung von Reduktionsmethoden für Wintersportler·innen zu vereinfachen, können die Informationen aus LLB computergestützt mit den Geländeeigenschaften ausgewertet und direkt in einer Karte dargestellt werden. Skitourenguru, beispielsweise, berechnet das Lawinenrisiko entlang vorgegebener Routen und stellt diese in einer 2D Karte dar. Im Vergleich zu 2D Karten erleichtert eine dreidimensionale Darstellung jedoch die Interpretation des Geländes und das Finden von Routen. Unsere Hypothese ist daher, dass eine direkte Visualisierung des Lawinenrisikos auf einer detaillierten 3D Karte die Identifikation von potenziell kritischen Regionen einer Route in der Planungsphase, sowie deren Wiedererkennung während der Tour, erleichtert.\nWir stellen eine integrierte 3D Risikovisualisierung vor, welche Daten aus dem aktuellen LLB mit einem hochauflösenden Geländemodell kombiniert und existierende Reduktionsmethoden in Echtzeit auswertet, um das Ergebnis auf einer interaktiven Webseite zu visualisieren.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2076, "image_height": 1192, "name": "eschner-2023-evl-.png", "type": "image/png", "size": 4716902, "path": "Publication:eschner-2023-evl", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 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"Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", 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"thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/", "__class": "Publication" }, { "id": "eschner-2023-ims", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/187705", "title": "Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations", "date": "2023-06", "abstract": "3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators’ goals are to convey a sense of the scene’s overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers’ attention through the story, such as highlighting, halos – or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers’ ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users’ ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1400, "image_height": 1080, "name": "eschner-2023-ims-teaser.png", "type": "image/png", "size": 1527403, "path": "Publication:eschner-2023-ims", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-ims/eschner-2023-ims-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-ims/eschner-2023-ims-teaser:thumb{{size}}.png" }, "sync_repositum_override": "event,lecturer,location,projects", "repositum_presentation_id": null, "authors": [ { "id": 1653, "drupal_id": 1354, "orcid_id": "0009-0001-6784-8503", "website": "https://www.cg.tuwien.ac.at/staff/JohannesEschner", "firstname": "Johannes", "lastname": "Eschner", "searchname": "Johannes Eschner", "is_teacher": false, "name": "Johannes Eschner", "publications": [ "ESCHNER-2019-GDT", "eschner-2023-evl", "eschner-2023-ims", "eschner-2023-rar", "eschner-2025-ide", "eschner-blur-2022", "grammatikaki-2025-htr", "huber-2025-eed" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "eschner-2023-evl", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "eschner-2023-ims", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "eschner-2025-ide", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1653", "__class": "Author" }, { "id": 935, "drupal_id": 45, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/PeterMindek", "firstname": "Peter", "lastname": "Mindek", "searchname": "Peter Mindek", "is_teacher": false, "name": "Peter Mindek", "publications": [ "eschner-2023-ims", 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Our goal is to support biological researches in molecular biology and phenotyping communities by developing visualizations that complement statistical methodology and wet lab with visual data analysis. Our tools allow the domain experts to see the key information they are looking for that relates to the studied structure, such as tunnels in molecules on the nanoscale or root shape distributions on the macroscopic magnification levels.\nEqually important is the role of visualization in explaining certain phenomenon to a larger audience. Biologists create large structural models that reflect the state-of-the-art knowledge about a certain living organism. This form of captured knowledge can be effectively communicated with interactive visualization, especially the aspect of understanding interplay of multiple spatial and temporal scales. With our visualization technology audience can therefore seamlessly zoom from zoom level when seeing entire bacteria down to its atomic detail.\n", "website": "https://www.cg.tuwien.ac.at/research/area/BioVis", "teaser": { "name": "teaser.png", "path": "research_area:BioVis", "type": "image/png", "size": 16330957, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/BioVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "amirkhanov-2019-manylands", "byska-2019-mdfc", "cellVIEW_2015", "cmolik-2020-tvcg", "Dietrich_1", "dietrich-2016-viseq2", "Ehlers_PhD", "ehlers-2025-ait", "eschner-2023-ims", "eschner-blur-2022", "Escribano_2017", "Furmanova_2018", "Gehrer-2017", "Gehrer-2017-molmach", "glinzner-2016-tex", "Halladjian_2020", "kapferer-2017-godot", "klein_2017_IM", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koch-2018-sso", "Kolesar-Fractional-2016", "kolesar-ivan-2014-polymers", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kozlikova-Visualization-2016b", "Krone2016VABC", "Kugler_2019", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lipp-2017-mgpu", "lipp-2017-vulkan", "Manczarski-2016", "manylands_award", "Miao_2019", "miao_inria_2017", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-xmas-card-2016", "Mitterhofer_2017", "nguyen_2020-covid", "Nowak_2020", "raidou_2019_springer", "raidou_shonan167", "raidou2018visualflatter", "rasch-2016-imgses", "Reisacher2016", "rinortner_susanne-2019-vpicc", "Sarkis_2021", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "stappen_2017_animatedcomic", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "trautner-2018-imd", "unger-2019_vcp", "vad_2015_phd", "vad_viktor-2017-WVE", "vad-2016-bre", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "waldin-2019-ccm", "waldner-2014-af", "Weissenboeck_2019", "wu-2017-dagstuhl", "wu-2018-metabo", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg" ], "projects": [ "BioNetIllustration", "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=BioVis", "__class": "ResearchArea" }, { "id": "IllVis", "drupal_id": 4131, "name": "Illustrative Visualization", "short_abstract": "In this research area, we develop rendering methods that are inspired by scientific illustrations, in order to make complex biological information more intuitive to understand and more pleasant to read.", "abstract": "Scientific illustration is concerned with researching ways in which complex biological and medical data can be abstracted in order to convey a desired information that would otherwise be impossible or hard to retrieve from the original data. Illustrators apply such techniques, for instance, to present non-important parts of an organ in a simplified way, while using detailed representations to put the focus of attention on a region where a tumor might be situated.\n\nTo identify such meaningful visual abstractions for visualization, their effectiveness for human perception and cognition has to be assessed, and they have to be formalized in order to make them applicable in rendering algorithms. In this project, we try to advance the understanding of procedural visual abstraction of complex physiological processes that describe the complex machinery of biological life.", "website": "https://www.cg.tuwien.ac.at/research/area/IllVis", "teaser": { "name": "teaser.jpg", "path": "research_area:IllVis", "type": "image/jpeg", "size": 34861, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/IllVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "antes-2026-maa", "Antonini2020", "Balabanian-2010-IIV", "Balduz_01", "Bayat_2019", "Bechtold2020", "Birkeland-2012-IMC", "brandstaetter_01", "bruckner-2005-ICV", "bruckner-2006-ICE", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2010-HVC", "bruckner-2010-IFC", "cellVIEW_2015", "Chen-Information-2016", "Deutsch-2018", "Dhanoa_2021", "Dhanoa2024", "dietrich-2016-viseq2", "donabauer_2019_1", "eg-tut2005-iv", "eschner-2023-ims", "Essler2022", "gangl-2018-gpsart", "Gehrer_Daniel_CUI", "Gehrer-2017-molmach", "gerl-2006-vhi", "Gogel2020", "Groeller_2011_IPV", "Groeller_2016_I3", "Groeller_2016_I6", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_V1_2020", "groeller-2006-ivt", "groeller-2007-fci", "groeller-2008-irs", "Groeller-2010-IFI", "gruber-2025-vps", "Halladjian_2020", "haushofer-2018-skinning", "Heim_2020", "horvath-2018-ism", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Knapp_2017_CVR", "Koeppel2020", "kolesar-ivan-2014-polymers", "Konev-FCV2018", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "kroesl_x_card_2017", "Lackner_2022", "Langer_Edith_IR1", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "Leutschacher_2022", "ludwig-2012-MT", "Manczarski-2016", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "Melo_2018_11", "Meusburger_2020", "Miao_2019", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao2018Dimsum", "Michl_2017_CSV", "mindek-2015-mc", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-xmas-card-2016", "Moellinger_Christian_IDE2", "Niedermayer_2018", "Ortner_PhD", "Ortner2020", "pahr-2021-vologram", "Panayotov", "patel_daniel_2007_IRSD", "Pauschenwein_2022", "Peter_2012_AIV", "plank-2017-sldg", "prost-2016-molecule", "purgathofer-2008-cgi", "Radwan-2017-Occ", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-VF", "Reichinger_2016", "Reichinger_Fuhrmann_2016", "Reisacher_Matthias_CPW", "Reisacher2016", "Riegelnegg_2019", "rippl-2023-atg", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "schindler_2020vis", "schmidlehner2021", "scholz_2021", "Schrempf_2020", "schultes_lampi", "seyfert-2017", "sikachev-2010-ill_vis_vol_ren", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "Spechtenhauser_Florian_2016", "stappen_2017_animatedcomic", "Stoll2020", "sturl_2016_dynlabels", "Swoboda2020", "szabo_patrik-2022-tour", "takahashi-2019-acdt", "Thomas-2018", "TR-186-2-07-08", "Troidl_2021", "Troidl_2022", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "Unger2020", "Vasileva_2022", "Viola_Ivan_2013_SVA", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "viola-evr", "Viola-Pondering-2017", "Viola-vistutillustrativevis", "Waldin_Nicholas_2016_Colormaps", "waldin-2019-ccm", "Waldner_2017_11", "Wihann_2017", "Woschizka_2021", "wu-2019-bmc", "wu-2019-report", "Zaufel_2021", "zechmeister2020", "Zeng_2021", "Zusag-2017-Bach" ], "projects": [ "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=IllVis", "__class": "ResearchArea" }, { "id": "Perception", "drupal_id": 4136, "name": "Perception", "short_abstract": "In this area, our research focuses on understanding and using the capabilities of human perception for designing high-quality graphics algorithms and effective visualizations.", "abstract": "In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
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This can lead to direct or indirect bias in downstream applications. In this work, we present a method for interactive visual exploration of indirect multiclass bias learned by contextual word embeddings. 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Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
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The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
", "abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
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", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "Information visualization", "Aggregate visualization", "Scalable visualization", "WebGPU" ], "weblinks": [ { "href": "https://diglib.eg.org/xmlui/handle/10.2312/evp20231079", "caption": "Eurographics Digital Library", "description": null, "main_file": 0 }, { "href": "https://ccexplorer.github.io/", "caption": "Climate Change Explorer", "description": "online demo", "main_file": 1 }, { "href": "https://cde.gkdev.at/cde/?db=cdata_interp_2bit_0.lzma", "caption": "large demo", "description": "Demo with more data points", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "extended abstract", "main_file": true, "use_in_gallery": false, "access": "public", "name": "webGPU_aggregateVis-2023-extended abstract.pdf", "type": "application/pdf", "size": 1466660, "path": 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humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
", "abstract": "In many domains, such as biology, chemistry, medicine, and the humanities, large amounts of data exist. Visual exploratory analysis of these data is often not practicable due to their size and their unstructured nature. Traditional machine learning (ML) requires large-scale labeled training data and a clear target definition, which is typically not available when exploring unknown data. For such large-scale, unstructured, open-ended, and domain-specific problems, we need an interactive approach combining the strengths of ML and human analytical skills into a unified process that helps users to \"detect the expected and discover the unexpected\".
We hypothesize that humans and machines can learn jointly from the data and from each other during exploratory data analysis. We further hypothesize that this joint learning enables a new visual analytics approach that reveals how users' incrementally growing insights fit the data, which will foster questioning and reframing.
We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data. In contrast to existing approaches, we investigate data exploration from a new perspective that focuses on the discovery and definition of complex structural information from the data rather than primarily on the model (as in ML) or on the data itself (as in visualization).
First, the conceptual framework of JDE introduces a novel knowledge modeling approach for visual analytics based on interactive ML that incrementally captures potentially complex, yet interpretable concepts that users expect or have learned from the data. Second, it proposes an intelligent agent that elicits information fitting the users' expectations and discovers what may be unexpected for the users. Third, it relies on a new visualization approach focusing on how the large-scale data fits the users' knowledge and expectations, rather than solely the data. Fourth, this leads to novel exploratory data analysis techniques -- an interactive interplay between knowledge externalization, machine-guided data inspection, questioning, and reframing.
The project is a joint collaboration between researchers from TU Wien (Manuela Waldner) and the University of Applied Sciences St. Pölten (Matthias Zeppelzauer), Austria, who contribute and join their complementary expertise on information visualization, visual analytics, and interactive ML.
FWF Stand-alone project P 36453
DOI: 10.55776/P36453
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When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1604, "image_height": 942, "name": "sMolBoxes_2022-teaser.png", "type": "image/png", "size": 417094, "path": "Publication:sMolBoxes_2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/sMolBoxes_2022-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/sMolBoxes_2022-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 5182, "drupal_id": null, "orcid_id": null, "website": null, 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Our goal is to support biological researches in molecular biology and phenotyping communities by developing visualizations that complement statistical methodology and wet lab with visual data analysis. Our tools allow the domain experts to see the key information they are looking for that relates to the studied structure, such as tunnels in molecules on the nanoscale or root shape distributions on the macroscopic magnification levels.\nEqually important is the role of visualization in explaining certain phenomenon to a larger audience. Biologists create large structural models that reflect the state-of-the-art knowledge about a certain living organism. This form of captured knowledge can be effectively communicated with interactive visualization, especially the aspect of understanding interplay of multiple spatial and temporal scales. With our visualization technology audience can therefore seamlessly zoom from zoom level when seeing entire bacteria down to its atomic detail.\n", "website": "https://www.cg.tuwien.ac.at/research/area/BioVis", "teaser": { "name": "teaser.png", "path": "research_area:BioVis", "type": "image/png", "size": 16330957, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/BioVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "amirkhanov-2019-manylands", "byska-2019-mdfc", "cellVIEW_2015", "cmolik-2020-tvcg", "Dietrich_1", "dietrich-2016-viseq2", "Ehlers_PhD", "ehlers-2025-ait", "eschner-2023-ims", "eschner-blur-2022", "Escribano_2017", "Furmanova_2018", "Gehrer-2017", "Gehrer-2017-molmach", "glinzner-2016-tex", "Halladjian_2020", "kapferer-2017-godot", "klein_2017_IM", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koch-2018-sso", "Kolesar-Fractional-2016", "kolesar-ivan-2014-polymers", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kozlikova-Visualization-2016b", "Krone2016VABC", "Kugler_2019", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lipp-2017-mgpu", "lipp-2017-vulkan", "Manczarski-2016", "manylands_award", "Miao_2019", "miao_inria_2017", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-xmas-card-2016", "Mitterhofer_2017", "nguyen_2020-covid", "Nowak_2020", "raidou_2019_springer", "raidou_shonan167", "raidou2018visualflatter", "rasch-2016-imgses", "Reisacher2016", "rinortner_susanne-2019-vpicc", "Sarkis_2021", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "stappen_2017_animatedcomic", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "trautner-2018-imd", "unger-2019_vcp", "vad_2015_phd", "vad_viktor-2017-WVE", "vad-2016-bre", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "waldin-2019-ccm", "waldner-2014-af", "Weissenboeck_2019", "wu-2017-dagstuhl", "wu-2018-metabo", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg" ], "projects": [ "BioNetIllustration", "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=BioVis", "__class": "ResearchArea" }, { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "Molecular Dynamics", "structure", "node-based visualization", "progressive analytics", "proteins", "Analytical models", "Biological system modeling", "Three-dimensional displays", "Computational modeling", "Task analysis", "Animation" ], "weblinks": [ { "href": "https://arxiv.org/abs/2209.11771", "caption": "arxiv", "description": null, "main_file": 1 } ], "files": [ { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1604, "image_height": 942, "name": "sMolBoxes_2022-teaser.png", "type": "image/png", "size": 417094, "path": "Publication:sMolBoxes_2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/sMolBoxes_2022-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/sMolBoxes_2022-teaser:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/sMolBoxes_2022/", "__class": "Publication" }, { "id": "grossmann-2022-conceptSplatters", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/152292", "title": "Concept splatters: Exploration of latent spaces based on human interpretable concepts", "date": "2022-04", "abstract": "Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present “concept splatters” as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1055, "image_height": 794, "name": "grossmann-2022-conceptSplatters-teaser.png", "type": "image/png", "size": 1013977, "path": "Publication:grossmann-2022-conceptSplatters", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/grossmann-2022-conceptSplatters/grossmann-2022-conceptSplatters-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/grossmann-2022-conceptSplatters/grossmann-2022-conceptSplatters-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,projects", "repositum_presentation_id": null, "authors": [ { "id": 1366, "drupal_id": 1304, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/NicolasGrossmann", "firstname": "Nicolas", "lastname": "Grossmann", "searchname": "Nicolas Grossmann", "is_teacher": false, "name": "Nicolas Grossmann", "publications": [ "furmanova_2020", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2016-baa", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "Iijima-2020-iV", "Kuroko-2020-iV", "raidou_2019_pelvisrunner", "raidou_visgap2020", "raidou2018visualflatter", "wu-2021", "wu-2021-vi" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "mayr-2021-wec", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "raidou_2019_pelvisrunner", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1366", "__class": "Author" }, { "id": 166, "drupal_id": 111, "orcid_id": "0000-0002-8569-4149", "website": 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"https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/grossmann-2022-conceptSplatters/", "__class": "Publication" }, { "id": "Alharbi_2021", "type_id": "journalpaper_notalk", "tu_id": 300116, "repositum_id": "20.500.12708/138522", "title": "Nanotilus: Generator of Immersive Guided-Tours in Crowded 3D Environments", "date": "2021-12-09", "abstract": "Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This paper proposesNanotilus---a new visibility and guidance approach for very dense environments that generates an endoscopic inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multiscale, multishell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models -- SARS-CoV-2 and HIV viruses. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 540, "image_height": 273, "name": "Alharbi_2021-Image.JPG", "type": "image/jpeg", "size": 46269, "path": "Publication:Alharbi_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1791, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Ruwayda", "lastname": "Alharbi", "searchname": "Ruwayda Alharbi", "is_teacher": false, "name": "Ruwayda Alharbi", "publications": [ "Alharbi_2021", "nguyen_2020-covid" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1791", "__class": "Author" }, { "id": 1789, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Ondrej", "lastname": "Strnad", "searchname": "Ondrej Strnad", "is_teacher": false, "name": "Ondrej Strnad", "publications": [ "Alharbi_2021", "kouril-2021-molecumentary", "nguyen_2020-covid" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1789", "__class": "Author" }, { "id": 1577, "drupal_id": 1303, "orcid_id": "0000-0003-3996-8337", "website": "https://www.cg.tuwien.ac.at/staff/LauraLuidolt", "firstname": "Laura R.", "lastname": "Luidolt", "searchname": "Laura R. 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"kouril-2021-molecumentary", "Kozlikova-Visualization-2016b", "Krone2016VABC", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "li-2019-gdc", "Masterthesis-Viola", "Mazza_2020", "Miao_2019", "miao_nantech_2019", "miao_nar_2020", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2015-mc", "mindek-2017-marion", "mindek-2017-virtualcell", "Neumann-2002-Fea", "nguyen_2020-covid", "patel-2010-SVV", "phd-viola", "polatsek-2018-stv", "Rautek-2008-kav", "Rautek-2008-VF", "Rautek06Vis", "Reisacher2016", "ruiz-2008-OVR", "ruiz-2008-SEV", "Sarkis_2021", "Sbardellati-2019-vcbm", "sbert-2017-sa_course_0023", "seyfert-2017", "solteszova-2010-MOS", "Solteszova2016", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "toth-2007-ndd", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "tut-eg-2006", "tut-siggraph-2006", 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"Plank_Pascal_2015_HVP", "field_id": "supervisor", "ordernum": 2 }, { "publ_id": "plank-2017-sldg", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "puenguentzky-2014-ht", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "raidou_vcbm14", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "rasch-2016-imgses", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "rautek-2009-vmv", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "Reisacher_Matthias_CPW", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "sbardellati_ba_2018", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "sifuentes-2018-crowd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "sorger-2017-thesis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "textures-3d-printing", "field_id": "supervisor", "ordernum": 2 }, { "publ_id": "Thurner-2005-IVR", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "vad_2015_phd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Viola_Ivan_2013_HQ3", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "Viola-05-Smart", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "viola-2007-ort", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "Viola-Pondering-2017", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "vis-foa", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldin-2017-thesis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Wihann_2017", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=171", "__class": "Author" } ], "doi": "10.1109/TVCG.2021.3133592", "first_published": "2021-12-09", "journal": "IEEE Transactions on Visualization and Computer Graphics", "open_access": "yes", "pages_from": "1", "pages_to": "16", "research_areas": [ { "id": "BioVis", "drupal_id": 4127, "name": "Biological Data Visualization", "short_abstract": "In this research area, we develop new visualization techniques to support biologists in data analysis and create visualizations to disseminate scientific discoveries in biology.", "abstract": "In modern biological research domain experts have to deal with complex and large data collections and simulations. Our goal is to support biological researches in molecular biology and phenotyping communities by developing visualizations that complement statistical methodology and wet lab with visual data analysis. Our tools allow the domain experts to see the key information they are looking for that relates to the studied structure, such as tunnels in molecules on the nanoscale or root shape distributions on the macroscopic magnification levels.\nEqually important is the role of visualization in explaining certain phenomenon to a larger audience. Biologists create large structural models that reflect the state-of-the-art knowledge about a certain living organism. This form of captured knowledge can be effectively communicated with interactive visualization, especially the aspect of understanding interplay of multiple spatial and temporal scales. With our visualization technology audience can therefore seamlessly zoom from zoom level when seeing entire bacteria down to its atomic detail.\n", "website": "https://www.cg.tuwien.ac.at/research/area/BioVis", "teaser": { "name": "teaser.png", "path": "research_area:BioVis", "type": "image/png", "size": 16330957, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/BioVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "amirkhanov-2019-manylands", "byska-2019-mdfc", "cellVIEW_2015", "cmolik-2020-tvcg", "Dietrich_1", "dietrich-2016-viseq2", "Ehlers_PhD", "ehlers-2025-ait", "eschner-2023-ims", "eschner-blur-2022", "Escribano_2017", "Furmanova_2018", "Gehrer-2017", "Gehrer-2017-molmach", "glinzner-2016-tex", "Halladjian_2020", "kapferer-2017-godot", "klein_2017_IM", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koch-2018-sso", "Kolesar-Fractional-2016", "kolesar-ivan-2014-polymers", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kozlikova-Visualization-2016b", "Krone2016VABC", "Kugler_2019", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lipp-2017-mgpu", "lipp-2017-vulkan", "Manczarski-2016", "manylands_award", "Miao_2019", "miao_inria_2017", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-xmas-card-2016", "Mitterhofer_2017", "nguyen_2020-covid", "Nowak_2020", "raidou_2019_springer", "raidou_shonan167", "raidou2018visualflatter", "rasch-2016-imgses", "Reisacher2016", "rinortner_susanne-2019-vpicc", "Sarkis_2021", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "stappen_2017_animatedcomic", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "trautner-2018-imd", "unger-2019_vcp", "vad_2015_phd", "vad_viktor-2017-WVE", "vad-2016-bre", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "waldin-2019-ccm", "waldner-2014-af", "Weissenboeck_2019", "wu-2017-dagstuhl", "wu-2018-metabo", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg" ], "projects": [ "BioNetIllustration", "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=BioVis", "__class": "ResearchArea" }, { "id": "IllVis", "drupal_id": 4131, "name": "Illustrative Visualization", "short_abstract": "In this research area, we develop rendering methods that are inspired by scientific illustrations, in order to make complex biological information more intuitive to understand and more pleasant to read.", "abstract": "Scientific illustration is concerned with researching ways in which complex biological and medical data can be abstracted in order to convey a desired information that would otherwise be impossible or hard to retrieve from the original data. Illustrators apply such techniques, for instance, to present non-important parts of an organ in a simplified way, while using detailed representations to put the focus of attention on a region where a tumor might be situated.\n\nTo identify such meaningful visual abstractions for visualization, their effectiveness for human perception and cognition has to be assessed, and they have to be formalized in order to make them applicable in rendering algorithms. In this project, we try to advance the understanding of procedural visual abstraction of complex physiological processes that describe the complex machinery of biological life.", "website": "https://www.cg.tuwien.ac.at/research/area/IllVis", "teaser": { "name": "teaser.jpg", "path": "research_area:IllVis", "type": "image/jpeg", "size": 34861, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/IllVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "antes-2026-maa", "Antonini2020", "Balabanian-2010-IIV", "Balduz_01", "Bayat_2019", "Bechtold2020", "Birkeland-2012-IMC", "brandstaetter_01", "bruckner-2005-ICV", "bruckner-2006-ICE", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2010-HVC", "bruckner-2010-IFC", "cellVIEW_2015", "Chen-Information-2016", "Deutsch-2018", "Dhanoa_2021", "Dhanoa2024", "dietrich-2016-viseq2", "donabauer_2019_1", "eg-tut2005-iv", "eschner-2023-ims", "Essler2022", "gangl-2018-gpsart", "Gehrer_Daniel_CUI", "Gehrer-2017-molmach", "gerl-2006-vhi", "Gogel2020", "Groeller_2011_IPV", "Groeller_2016_I3", "Groeller_2016_I6", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_V1_2020", "groeller-2006-ivt", "groeller-2007-fci", "groeller-2008-irs", "Groeller-2010-IFI", "gruber-2025-vps", "Halladjian_2020", "haushofer-2018-skinning", "Heim_2020", "horvath-2018-ism", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Knapp_2017_CVR", "Koeppel2020", "kolesar-ivan-2014-polymers", "Konev-FCV2018", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "kroesl_x_card_2017", "Lackner_2022", "Langer_Edith_IR1", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "Leutschacher_2022", "ludwig-2012-MT", "Manczarski-2016", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "Melo_2018_11", "Meusburger_2020", "Miao_2019", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao2018Dimsum", "Michl_2017_CSV", "mindek-2015-mc", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-xmas-card-2016", "Moellinger_Christian_IDE2", "Niedermayer_2018", "Ortner_PhD", "Ortner2020", "pahr-2021-vologram", "Panayotov", "patel_daniel_2007_IRSD", "Pauschenwein_2022", "Peter_2012_AIV", "plank-2017-sldg", "prost-2016-molecule", "purgathofer-2008-cgi", "Radwan-2017-Occ", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-VF", "Reichinger_2016", "Reichinger_Fuhrmann_2016", "Reisacher_Matthias_CPW", "Reisacher2016", "Riegelnegg_2019", "rippl-2023-atg", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "schindler_2020vis", "schmidlehner2021", "scholz_2021", "Schrempf_2020", "schultes_lampi", "seyfert-2017", "sikachev-2010-ill_vis_vol_ren", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "Spechtenhauser_Florian_2016", "stappen_2017_animatedcomic", "Stoll2020", "sturl_2016_dynlabels", "Swoboda2020", "szabo_patrik-2022-tour", "takahashi-2019-acdt", "Thomas-2018", "TR-186-2-07-08", "Troidl_2021", "Troidl_2022", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "Unger2020", "Vasileva_2022", "Viola_Ivan_2013_SVA", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "viola-evr", "Viola-Pondering-2017", "Viola-vistutillustrativevis", "Waldin_Nicholas_2016_Colormaps", "waldin-2019-ccm", "Waldner_2017_11", "Wihann_2017", "Woschizka_2021", "wu-2019-bmc", "wu-2019-report", "Zaufel_2021", "zechmeister2020", "Zeng_2021", "Zusag-2017-Bach" ], "projects": [ "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=IllVis", "__class": "ResearchArea" }, { "id": "VR", "drupal_id": 4126, "name": "Virtual Reality", "short_abstract": "In this area, we focus on user experiences and rendering algorithms for virtual reality environments, including methods to navigate and collaborate in VR, foveated rendering, exploit human perception and simulate visual deficiencies.
", "abstract": "In this area, we focus on user experiences and rendering algorithms for virtual reality environments, including methods to navigate and collaborate in VR, foveated rendering, exploit human perception and simulate visual deficiencies.
", "website": "https://www.cg.tuwien.ac.at/research/area/VR", "teaser": { "name": "teaser.jpg", "path": "research_area:VR", "type": "image/jpeg", "size": 191119, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/VR/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend", "vis" ], "publications": [ "adolf-2019-jug", "Alharbi_2021", "antes-2026-maa", "Bayer_Alexander_2021_FTiVR", "Erhardt_2025_Scutes", "gall-2025-marv", "Goellner_2019-ABC", "gramegna-rfr", "hladky-2022-QS", "huber-2025-eed", "huber-2025-esl", "Kan_Peter-2021-MDPI", "kan-2019-dli", "Kathi-2018-VRB", "Kovacs_2020", "Kroesl_2020_11_09", "kroesl-2019-MoD", "KROESL-2020-SVI", "kroesl-2020-XREye", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "medeiros-2023-gwf", "Mirzaei_Mohammadreza_2020-EVR", "Mortezapoor2021ssc", "panfili-2019-VAVR", "perl_oskar_2021_DVR", "pirch_2021_VRN", "podkosova_2022_bimflexi-vr", "Prossenitsch_2019-1", "reimer-2020-CBG", "reimer-2021-CVR", "Schoenauer_2020_POI", "schuller_reichl-2019-avt", "Sebernegg2020", "sorger-2021-egonet", "Sprung_2021", "Vasylevska2020VRB", "Vonach021", "walchhofer_ar_flowmaps", "walchhofer-tso" ], "projects": [ "BimFlexi", "d4322", "d9275", "EU U-CREATE", "EvaluARte", "MARG", "Superhumans" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=VR", "__class": "ResearchArea" } ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 540, "image_height": 273, "name": "Alharbi_2021-Image.JPG", "type": "image/jpeg", "size": 46269, "path": "Publication:Alharbi_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Image.JPG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Alharbi_2021-Paper.pdf", "type": "application/pdf", "size": 14837965, "path": "Publication:Alharbi_2021", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/Alharbi_2021-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "vis", "drupal_id": 1354, "name": "Visualization Group", "website": "https://www.cg.tuwien.ac.at/group/Visualization-Group", "projects": [ "ADAPT", "AngioVis", "Animated Cell Tab development", "AutARG", "avr", "BioNetIllustration", "COMRADE", "d10027", "d10162", "d10164", "d10206", "d10214", "d10413", "d10450", "d10489", "d4328", "d4335", "d4534", "d9275", "d9282", "d9522", "d9555", "d9671", "d9951", "deskollage", "diagvis", "exvisation", "Illustrare", "illvisation", "KASI", "ManyViews", "NeuroViewer", "ProcessIllVis", "ProKeyAnim", "PVG", "Scale-VS", "Scenario Pool", "Semantic Steering", "Sim-CT", "Smart-CT", "Smile Analytics", "ViMaL", "VISAR" ], "research_areas": [ "BioVis", "IllVis", "InfoVis", "MedVis", "NetVis", "Perception", "VisMat", "VR" ], "publications": [ "2013_Viola_Ivan_2013_MTS", "2015-image-coll", "2017-kouril-illcell", "2019-ic", "AA2020", "aigner-2025-vhv", "alacruzECR2004", "alacruzVisSym2004", "Alharbi_2021", "Alsallakh_2012_RCW", "alsallakh-2009-iva", "altendorfer-2008-vts", "amabili2021", "amirkhanov_2012", "amirkhanov_2012heur", "amirkhanov-2011", "amirkhanov-2014-ama", "amirkhanov-2018-withteeth", "amirkhanov-2021-diss", "amirkhanov2010AMA", "ammer-2008-lst", "An-2018-CHI", "AndreUrsits_2013_APP", "antes-2023-vhv", "Antonini2020", "arbesser_ba_2023", "Artner-2004-Hig", "Artner-2005-Hig", "Artner-2005-Thesis", "audioflow", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2007-ant", "balabanian-2008-hvv", "Balabanian-2010-IIV", "Balabanian-2010-PSDSV", "Balduz_01", "Basch_2011_ATA", "Bauer_David_2018", "Bauer_Johannes_2014_IWB", "bauer-2015-cbfd", "Bayat_2019", "bayer-2015-gapaot", "bbeer-2013", "Bechtold2020", "Beer_2011_DVE", "Beham_Michael_PSC", "beham-2014-cupid", "bernhard-2006-dvrcta", "bernhard-2014-GTOM", "bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", 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"pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", 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"waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/Alharbi_2021/", "__class": "Publication" }, { "id": "sorger-2021-egonet", "type_id": "journalpaper", "tu_id": 300416, "repositum_id": "20.500.12708/58630", "title": "Egocentric Network Exploration for Immersive Analytics", "date": "2021-10", "abstract": "To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of ``egocentrism'', where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber-sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber-sickness. Results show that a simple egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber-sickness. A distorted occlusion-free view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for egocentric network exploration, providing actionable insights on the benefits of the egocentric network exploration metaphor.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2921, "image_height": 735, "name": "sorger-2021-egonet-teaser.png", "type": "image/png", "size": 2079618, "path": "Publication:sorger-2021-egonet", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/sorger-2021-egonet-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1072, "drupal_id": 1056, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/JohannesSorger", "firstname": "Johannes", "lastname": "Sorger", "searchname": "Johannes Sorger", "is_teacher": false, "name": "Johannes Sorger", "publications": [ "Arleo-2019-vis", "klaffenboeck-2024-rva", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "miao_tvcg_2018", "mindek-2017-marion", "mindek-2017-virtualcell", "ortner-2016-tunnel", "ortner-2016-visaware", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "eiweck-hnv-2021", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "lippeck-2022-mna", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Manczarski-2016", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "sorger-2013-neuromap", "field_id": "lecturer", 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"https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1705", "__class": "Author" }, { "id": 1720, "drupal_id": 1549, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/PeterKan", "firstname": "Peter", "lastname": "Kán", "searchname": "Peter Kán", "is_teacher": false, "name": "Peter Kán", "publications": [ "adolf-2019-jug", "Honic_2021", "Kan_Peter-2021-BuildingMonitoring", "Kan_Peter-2021-MDPI", "kan-2019-dli", "Mirzaei_Mohammad-2021", "Mirzaei_Mohammad-2021-HeadUp", "Mirzaei_Mohammadreza_2020-EVR", "Mirzaei_Mohhamad-2021", "Reisinger_Julia-2021-parametricscript", "Reisinger-2021-JOBE", "Sebernegg2020", "sorger-2021-egonet" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "unterguggenberger-2016-realmar", "field_id": "co_supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1720", "__class": "Author" }, { "id": 853, "drupal_id": null, "orcid_id": null, 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", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "NetVis", "drupal_id": 9472, "name": "Network Visualization", "short_abstract": "This research area aims to investigate how to best visualize graph-typed data, both abstractly as well as in domain-specific applications.", "abstract": "Graph-typed data is ubiquitous, from logistical or transportation networks, through social or correspondence networks, all the way to more abstract biochemical interaction networks. Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" }, { "id": "VR", "drupal_id": 4126, "name": "Virtual Reality", "short_abstract": "In this area, we focus on user experiences and rendering algorithms for virtual reality environments, including methods to navigate and collaborate in VR, foveated rendering, exploit human perception and simulate visual deficiencies.
", "abstract": "In this area, we focus on user experiences and rendering algorithms for virtual reality environments, including methods to navigate and collaborate in VR, foveated rendering, exploit human perception and simulate visual deficiencies.
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"webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/sorger-2021-egonet/", "__class": "Publication" }, { "id": "grossmann-2021-layout", "type_id": "inproceedings", "tu_id": 300287, "repositum_id": "20.500.12708/58620", "title": "Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation", "date": "2021-10", "abstract": "In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model's accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model's accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.", "authors_et_al": false, "substitute": null, "main_image": { "description": "In both scatterplots shown here, the percentage of images with correctly predicted class labels (visualized as border color) is over 90%. We found that users can estimate these accuracies fairly well. Image complexity impacts overall performance, but the layout has very little effect on users’ estimations. 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"__class": "Author" }, { "id": 1690, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Michael", "lastname": "Sedlmair", "searchname": "Michael Sedlmair", "is_teacher": false, "name": "Michael Sedlmair", "publications": [ "2019-ic", "grossmann-2021-layout", "waldner-2021-leo" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "Ortner_PhD", "field_id": "reviewer_1", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1690", "__class": "Author" }, { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", 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By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
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We found that users can estimate these accuracies fairly well. Image complexity impacts overall performance, but the layout has very little effect on users’ estimations. 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"Interactive Analysis of CNN Robustness", "date": "2021-10", "abstract": "While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against.\nThis paper presents Perturber, a web-based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine-tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users' insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models. ", "authors_et_al": false, "substitute": null, "main_image": { "description": "Activations and feature visualizations for neurons associated with complex shapes and curvatures in layer mixed4a in the standard model. Note how rotating the input model causes activation changes for oriented shape detectors. 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"buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", 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The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. 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"wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/wu-2021-vi/", "__class": "Publication" }, { "id": "waldner-2021-leo", "type_id": "journalpaper_notalk", "tu_id": 301684, "repositum_id": "20.500.12708/138840", "title": "Linking unstructured evidence to structured observations", "date": "2021-01-14", "abstract": "Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user’s structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users’ sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users’ observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "observation graph", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1920, "image_height": 700, "name": "waldner-2021-leo-observation graph.png", "type": "image/png", "size": 523772, "path": "Publication:waldner-2021-leo", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/waldner-2021-leo/waldner-2021-leo-observation graph.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/waldner-2021-leo/waldner-2021-leo-observation graph:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": 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"Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/waldner-2021-leo/", "__class": "Publication" }, { "id": "reina-2020-mtv", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/141135", "title": "The moving target of visualization software for an increasingly complex world", "date": "2020-04", "abstract": "Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. 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We present a new interactive exploration interface for large, time-dependent bipartite graphs. We use two clustering techniques to build a hierarchical aggregation supporting different exploration strategies. Aggregated nodes and edges are visualized as linked lists with nested time series. We demonstrate two use cases: finding advertising expenses of public authorities following similar temporal patterns and comparing author-keyword co-occurrences across time. 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"supervisor", "ordernum": 0 }, { "publ_id": "wissmann-2022-alb", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Woschizka_2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "zambal-2005-3dact", "field_id": "supervisor", "ordernum": 2 }, { "publ_id": "zambal-2009-ami", "field_id": "co_supervisor", "ordernum": 1 }, { "publ_id": "Zaufel_2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "zauner-2013-svkinect", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "zechmeister2020", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Zhu-2014-flowvis", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=166", "__class": "Author" } ], "doi": "https://doi.org/10.1016/j.cola.2020.100959", "journal": "Journal of Computer Languages", "volume": "57", "research_areas": [ { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "NetVis", "drupal_id": 9472, "name": "Network Visualization", "short_abstract": "This research area aims to investigate how to best visualize graph-typed data, both abstractly as well as in domain-specific applications.", "abstract": "Graph-typed data is ubiquitous, from logistical or transportation networks, through social or correspondence networks, all the way to more abstract biochemical interaction networks. Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "Information visualization", "Bipartite graphs", "Clustering", "Time series data", "Insight-based evaluation" ], "weblinks": [ { "href": "https://www.sciencedirect.com/science/article/pii/S2590118420300198", "caption": "paper", "description": "Open Access article at ScienceDirect", "main_file": 1 } ], "files": [ { "description": "Dynamic BicFlows with nested time series visualization per cluster per set.", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1121, "image_height": 936, "name": "waldner-2020-tbg-teaser.png", "type": "image/png", "size": 250456, "path": "Publication:waldner-2020-tbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/waldner-2020-tbg-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/", "__class": "Publication" }, { "id": "sorger-2019-odn", "type_id": "inproceedings", "tu_id": 284199, "repositum_id": null, "title": "Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation", "date": "2019-12", "abstract": "Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly- or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing medical data.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "detail view", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1600, "image_height": 900, "name": "sorger-2019-odn-detail view.jpg", "type": "image/jpeg", "size": 203956, "path": "Publication:sorger-2019-odn", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/sorger-2019-odn/sorger-2019-odn-detail view:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1072, "drupal_id": 1056, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/JohannesSorger", "firstname": "Johannes", "lastname": "Sorger", "searchname": "Johannes Sorger", "is_teacher": false, "name": "Johannes Sorger", "publications": [ "Arleo-2019-vis", "klaffenboeck-2024-rva", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "miao_tvcg_2018", "mindek-2017-marion", "mindek-2017-virtualcell", "ortner-2016-tunnel", "ortner-2016-visaware", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "eiweck-hnv-2021", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "lippeck-2022-mna", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Manczarski-2016", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "sorger-2013-neuromap", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "sorger-2015-litevis", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "sorger-2015-taxintec", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "sorger-2019-odn", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "sorger-2021-egonet", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "stappen_2017_animatedcomic", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1072", "__class": "Author" }, { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "geymayer-2017-std", "grammatikaki-2025-htr", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "heim-2024-accustripes", "indirectBiasLanguageModels-2023", "komon-2025-dco", "komon-2025-webigeo", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "matt-2024-cvil", "matt-2025-scv", "mazurek-2018-veq", "pahr-2024-ieo", "polatsek-2018-stv", "reina-2020-mtv", "sietzen-2021-perturber", "sietzen-ifv-2019", "sMolBoxes_2022", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "tekaya-2025-amo", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "webGPU_aggregateVis-2023", "wu-2021", "wu-2021-vi" ], "publications_first": [], "project_leader": [ "d9282", "d9522", "d9555", "deskollage" ], "publication_field": [ { "publ_id": "arbesser_ba_2023", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bayat-2022-mva", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "boesch_2014_browserHistoryVis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "breitenecker-2020-bct", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bugnar-esk-2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "casellato-2016-pkg", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "cizmic-2018-evd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "da_cunha_melo-2023-c3m", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "donabauer-2015-asc", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "dworschak-2016-szcm", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "dworschak-2024-aml", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "edlinger-2018-vwr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "eitler-2024-sos", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "eiweck-hnv-2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "ellhotka-2025-ipo", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "eschner-2023-rar", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "eschner-blur-2022", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "georgiev-2019-cbg", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Gogel2020", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "grammatikaki-thesis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "grossmann-2021-layout", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "gundacker-2020-wlm", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "gusenbauer-2018", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "heim-gall-2020-dde", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "hofmann_julia-2025", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "hromniak-2019-vcn", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "indirectBiasLanguageModels-2023", "field_id": "lecturer", "ordernum": 0 }, { 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", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "Immersive Network Analytics", "Web-Based Visualization", "Dynamic Graph Visualization" ], "weblinks": [ { "href": "https://arxiv.org/pdf/1910.06825.pdf", "caption": "arxiv preprint", "description": null, "main_file": 1 }, { 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"Bauer_Johannes_2014_IWB", "bauer-2015-cbfd", "Bayat_2019", "bayer-2015-gapaot", "bbeer-2013", "Bechtold2020", "Beer_2011_DVE", "Beham_Michael_PSC", "beham-2014-cupid", "bernhard-2006-dvrcta", "bernhard-2014-GTOM", "bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", 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exist in the form of interactive articles, e.g. DeepDream, Feature Visualization, The Building Blocks of Interpretability, Activation Atlas, Visualizing GoogLeNet Classes. They mostly rely on curated prerendered visualizations, additionally providing colab notebooks or public repositories allowing the reader to reproduce those results. While precalculated visualizations have many advantages (directability, more processing budget), they are always discretized samples of a continuous parameter space. In the spirit of Tensorflow Playground, this project aims at providing a fully interactive interface to some basic functionality of the originally Python-based Lucid library, roughly corresponding to the concepts presented in the “Feature Visualization\" article. The user is invited to explore the effect of parameter changes in a playful way and without requiring any knowledge of programming, enabled by an implementation on top of TensorFlow.js. Live updates of the generated input image as well as feature map activations should give the user a visual intuition to the otherwise abstract optimization process. 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null, "orcid_id": null, "website": null, "firstname": "Stefan", "lastname": "Sietzen", "searchname": "Stefan Sietzen", "is_teacher": false, "name": "Stefan Sietzen", "publications": [ "SIETZEN-2017-BBO", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "sietzen-2021-perturber", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "sietzen-ifv-2019", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1373", "__class": "Author" } ], "location": "Vancouver", "research_areas": [ { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. 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", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/2019-ic/", "__class": "Publication" }, { "id": "byska-2019-mdfc", "type_id": "journalpaper", "tu_id": 283263, "repositum_id": null, "title": "Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization", "date": "2019-06", "abstract": "Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 783, "image_height": 717, "name": "byska-2019-mdfc-teaser.jpg", "type": "image/jpeg", "size": 95308, "path": "Publication:byska-2019-mdfc", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-teaser.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1254, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Jan", "lastname": "Byska", "searchname": "Jan Byska", "is_teacher": false, "name": "Jan Byska", "publications": [ 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}, { "publ_id": "Viola_Ivan_2015_MCT", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1254", "__class": "Author" } ], "location": "Porto, Portugal", "number": "3", "pages_from": "441", "pages_to": "453", "volume": "38", "research_areas": [ { "id": "BioVis", "drupal_id": 4127, "name": "Biological Data Visualization", "short_abstract": "In this research area, we develop new visualization techniques to support biologists in data analysis and create visualizations to disseminate scientific discoveries in biology.", "abstract": "In modern biological research domain experts have to deal with complex and large data collections and simulations. Our goal is to support biological researches in molecular biology and phenotyping communities by developing visualizations that complement statistical methodology and wet lab with visual data analysis. Our tools allow the domain experts to see the key information they are looking for that relates to the studied structure, such as tunnels in molecules on the nanoscale or root shape distributions on the macroscopic magnification levels.\nEqually important is the role of visualization in explaining certain phenomenon to a larger audience. Biologists create large structural models that reflect the state-of-the-art knowledge about a certain living organism. This form of captured knowledge can be effectively communicated with interactive visualization, especially the aspect of understanding interplay of multiple spatial and temporal scales. With our visualization technology audience can therefore seamlessly zoom from zoom level when seeing entire bacteria down to its atomic detail.\n", "website": "https://www.cg.tuwien.ac.at/research/area/BioVis", "teaser": { "name": "teaser.png", "path": "research_area:BioVis", "type": "image/png", "size": 16330957, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/BioVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "amirkhanov-2019-manylands", "byska-2019-mdfc", "cellVIEW_2015", "cmolik-2020-tvcg", "Dietrich_1", "dietrich-2016-viseq2", "Ehlers_PhD", "ehlers-2025-ait", "eschner-2023-ims", "eschner-blur-2022", "Escribano_2017", "Furmanova_2018", "Gehrer-2017", "Gehrer-2017-molmach", "glinzner-2016-tex", "Halladjian_2020", "kapferer-2017-godot", "klein_2017_IM", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koch-2018-sso", "Kolesar-Fractional-2016", "kolesar-ivan-2014-polymers", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kozlikova-Visualization-2016b", "Krone2016VABC", "Kugler_2019", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lipp-2017-mgpu", "lipp-2017-vulkan", "Manczarski-2016", "manylands_award", "Miao_2019", "miao_inria_2017", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-xmas-card-2016", "Mitterhofer_2017", "nguyen_2020-covid", "Nowak_2020", "raidou_2019_springer", "raidou_shonan167", "raidou2018visualflatter", "rasch-2016-imgses", "Reisacher2016", "rinortner_susanne-2019-vpicc", "Sarkis_2021", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "stappen_2017_animatedcomic", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "trautner-2018-imd", "unger-2019_vcp", "vad_2015_phd", "vad_viktor-2017-WVE", "vad-2016-bre", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "waldin-2019-ccm", "waldner-2014-af", "Weissenboeck_2019", "wu-2017-dagstuhl", "wu-2018-metabo", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg" ], "projects": [ "BioNetIllustration", "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=BioVis", "__class": "ResearchArea" }, { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "scientific visualization", "user centered design" ], "weblinks": [ { "href": "https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13701", "caption": "Wiley Online ", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "byska-2019-mdfc-paper.pdf", "type": "application/pdf", "size": 5805942, "path": "Publication:byska-2019-mdfc", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-paper:thumb{{size}}.png" }, { "description": "Domain expert report of findings", "filetitle": "supplement", 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"https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "byska-2019-mdfc-video.mp4", "type": "video/mp4", "size": 222623560, "path": "Publication:byska-2019-mdfc", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/byska-2019-mdfc-video:video.mp4" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/byska-2019-mdfc/", "__class": "Publication" }, { "id": "waldin-2019-ccm", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Cuttlefish: Color Mapping for Dynamic Multi‐Scale Visualizations", "date": "2019-03", "abstract": "Visualizations of hierarchical data can often be explored interactively. For example, in geographic visualization, there are continents, which can be subdivided into countries, states, counties and cities. Similarly, in models of viruses or bacteria at the highest level are the compartments, and below that are macromolecules, secondary structures (such as α‐helices), amino‐acids, and on the finest level atoms. Distinguishing between items can be assisted through the use of color at all levels. However, currently, there are no hierarchical and adaptive color mapping techniques for very large multi‐scale visualizations that can be explored interactively. We present a novel, multi‐scale, color‐mapping technique for adaptively adjusting the color scheme to the current view and scale. Color is treated as a resource and is smoothly redistributed. The distribution adjusts to the scale of the currently observed detail and maximizes the color range utilization given current viewing requirements. Thus, we ensure that the user is able to distinguish items on any level, even if the color is not constant for a particular feature. The coloring technique is demonstrated for a political map and a mesoscale structural model of HIV. The technique has been tested by users with expertise in structural biology and was overall well received.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Multiple color zoom levels. ", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 509, "image_height": 447, "name": "waldin-2019-ccm-teaser.png", "type": "image/png", "size": 690229, "path": "Publication:waldin-2019-ccm", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/waldin-2019-ccm/waldin-2019-ccm-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/waldin-2019-ccm/waldin-2019-ccm-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1339, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Nicholas", "lastname": "Waldin", "searchname": "Nicholas Waldin", "is_teacher": false, "name": "Nicholas Waldin", "publications": [ "waldin-2019-ccm" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "Plank_Pascal_2015_HVP", "field_id": "supervisor", "ordernum": 1 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Our goal is to support biological researches in molecular biology and phenotyping communities by developing visualizations that complement statistical methodology and wet lab with visual data analysis. Our tools allow the domain experts to see the key information they are looking for that relates to the studied structure, such as tunnels in molecules on the nanoscale or root shape distributions on the macroscopic magnification levels.\nEqually important is the role of visualization in explaining certain phenomenon to a larger audience. Biologists create large structural models that reflect the state-of-the-art knowledge about a certain living organism. This form of captured knowledge can be effectively communicated with interactive visualization, especially the aspect of understanding interplay of multiple spatial and temporal scales. With our visualization technology audience can therefore seamlessly zoom from zoom level when seeing entire bacteria down to its atomic detail.\n", "website": "https://www.cg.tuwien.ac.at/research/area/BioVis", "teaser": { "name": "teaser.png", "path": "research_area:BioVis", "type": "image/png", "size": 16330957, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/BioVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "amirkhanov-2019-manylands", "byska-2019-mdfc", "cellVIEW_2015", "cmolik-2020-tvcg", "Dietrich_1", "dietrich-2016-viseq2", "Ehlers_PhD", "ehlers-2025-ait", "eschner-2023-ims", "eschner-blur-2022", "Escribano_2017", "Furmanova_2018", "Gehrer-2017", "Gehrer-2017-molmach", "glinzner-2016-tex", "Halladjian_2020", "kapferer-2017-godot", "klein_2017_IM", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "koch-2018-sso", "Kolesar-Fractional-2016", "kolesar-ivan-2014-polymers", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kozlikova-Visualization-2016b", "Krone2016VABC", "Kugler_2019", "lemuzic_2015_timelapse", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lipp-2017-mgpu", "lipp-2017-vulkan", "Manczarski-2016", "manylands_award", "Miao_2019", "miao_inria_2017", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao_thesis_2019", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-xmas-card-2016", "Mitterhofer_2017", "nguyen_2020-covid", "Nowak_2020", "raidou_2019_springer", "raidou_shonan167", "raidou2018visualflatter", "rasch-2016-imgses", "Reisacher2016", "rinortner_susanne-2019-vpicc", "Sarkis_2021", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2013-neuromap", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "stappen_2017_animatedcomic", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "trautner-2018-imd", "unger-2019_vcp", "vad_2015_phd", "vad_viktor-2017-WVE", "vad-2016-bre", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "waldin-2019-ccm", "waldner-2014-af", "Weissenboeck_2019", "wu-2017-dagstuhl", "wu-2018-metabo", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg" ], "projects": [ "BioNetIllustration", "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=BioVis", "__class": "ResearchArea" }, { "id": "IllVis", "drupal_id": 4131, "name": "Illustrative Visualization", "short_abstract": "In this research area, we develop rendering methods that are inspired by scientific illustrations, in order to make complex biological information more intuitive to understand and more pleasant to read.", "abstract": "Scientific illustration is concerned with researching ways in which complex biological and medical data can be abstracted in order to convey a desired information that would otherwise be impossible or hard to retrieve from the original data. Illustrators apply such techniques, for instance, to present non-important parts of an organ in a simplified way, while using detailed representations to put the focus of attention on a region where a tumor might be situated.\n\nTo identify such meaningful visual abstractions for visualization, their effectiveness for human perception and cognition has to be assessed, and they have to be formalized in order to make them applicable in rendering algorithms. In this project, we try to advance the understanding of procedural visual abstraction of complex physiological processes that describe the complex machinery of biological life.", "website": "https://www.cg.tuwien.ac.at/research/area/IllVis", "teaser": { "name": "teaser.jpg", "path": "research_area:IllVis", "type": "image/jpeg", "size": 34861, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/IllVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "antes-2026-maa", "Antonini2020", "Balabanian-2010-IIV", "Balduz_01", "Bayat_2019", "Bechtold2020", "Birkeland-2012-IMC", "brandstaetter_01", "bruckner-2005-ICV", "bruckner-2006-ICE", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2010-HVC", "bruckner-2010-IFC", "cellVIEW_2015", "Chen-Information-2016", "Deutsch-2018", "Dhanoa_2021", "Dhanoa2024", "dietrich-2016-viseq2", "donabauer_2019_1", "eg-tut2005-iv", "eschner-2023-ims", "Essler2022", "gangl-2018-gpsart", "Gehrer_Daniel_CUI", "Gehrer-2017-molmach", "gerl-2006-vhi", "Gogel2020", "Groeller_2011_IPV", "Groeller_2016_I3", "Groeller_2016_I6", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_V1_2020", "groeller-2006-ivt", "groeller-2007-fci", "groeller-2008-irs", "Groeller-2010-IFI", "gruber-2025-vps", "Halladjian_2020", "haushofer-2018-skinning", "Heim_2020", "horvath-2018-ism", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Knapp_2017_CVR", "Koeppel2020", "kolesar-ivan-2014-polymers", "Konev-FCV2018", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "kroesl_x_card_2017", "Lackner_2022", "Langer_Edith_IR1", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "Leutschacher_2022", "ludwig-2012-MT", "Manczarski-2016", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "Melo_2018_11", "Meusburger_2020", "Miao_2019", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_stateKeylabTalk_2017", "miao2018Dimsum", "Michl_2017_CSV", "mindek-2015-mc", "mindek-2016-utah-talk", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-xmas-card-2016", "Moellinger_Christian_IDE2", "Niedermayer_2018", "Ortner_PhD", "Ortner2020", "pahr-2021-vologram", "Panayotov", "patel_daniel_2007_IRSD", "Pauschenwein_2022", "Peter_2012_AIV", "plank-2017-sldg", "prost-2016-molecule", "purgathofer-2008-cgi", "Radwan-2017-Occ", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-VF", "Reichinger_2016", "Reichinger_Fuhrmann_2016", "Reisacher_Matthias_CPW", "Reisacher2016", "Riegelnegg_2019", "rippl-2023-atg", "sbardellati_ba_2018", "Sbardellati-2019-vcbm", "schindler_2020vis", "schmidlehner2021", "scholz_2021", "Schrempf_2020", "schultes_lampi", "seyfert-2017", "sikachev-2010-ill_vis_vol_ren", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "Spechtenhauser_Florian_2016", "stappen_2017_animatedcomic", "Stoll2020", "sturl_2016_dynlabels", "Swoboda2020", "szabo_patrik-2022-tour", "takahashi-2019-acdt", "Thomas-2018", "TR-186-2-07-08", "Troidl_2021", "Troidl_2022", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "Unger2020", "Vasileva_2022", "Viola_Ivan_2013_SVA", "Viola_Ivan_CLD", "Viola_Ivan_IIP", "viola-evr", "Viola-Pondering-2017", "Viola-vistutillustrativevis", "Waldin_Nicholas_2016_Colormaps", "waldin-2019-ccm", "Waldner_2017_11", "Wihann_2017", "Woschizka_2021", "wu-2019-bmc", "wu-2019-report", "Zaufel_2021", "zechmeister2020", "Zeng_2021", "Zusag-2017-Bach" ], "projects": [ "Illustrare", "illvisation" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=IllVis", "__class": "ResearchArea" }, { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "multiscale visualization", "illustrative visualization", "molecular visualization" ], "weblinks": [ { "href": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.13611", "caption": "Open Access Article in Wiley Online Library", "description": null, "main_file": 1 } ], "files": [ { "description": "Multiple color zoom levels. ", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 509, "image_height": 447, "name": "waldin-2019-ccm-teaser.png", "type": "image/png", "size": 690229, "path": "Publication:waldin-2019-ccm", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/waldin-2019-ccm/waldin-2019-ccm-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/waldin-2019-ccm/waldin-2019-ccm-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "vis", "drupal_id": 1354, "name": "Visualization Group", "website": "https://www.cg.tuwien.ac.at/group/Visualization-Group", "projects": [ "ADAPT", "AngioVis", "Animated Cell Tab development", "AutARG", "avr", "BioNetIllustration", "COMRADE", "d10027", "d10162", "d10164", "d10206", "d10214", "d10413", "d10450", "d10489", "d4328", "d4335", "d4534", "d9275", "d9282", "d9522", "d9555", "d9671", "d9951", "deskollage", "diagvis", "exvisation", "Illustrare", "illvisation", "KASI", "ManyViews", "NeuroViewer", "ProcessIllVis", "ProKeyAnim", "PVG", "Scale-VS", "Scenario Pool", "Semantic Steering", "Sim-CT", "Smart-CT", "Smile Analytics", "ViMaL", "VISAR" ], "research_areas": [ "BioVis", "IllVis", "InfoVis", "MedVis", "NetVis", "Perception", "VisMat", "VR" ], "publications": [ "2013_Viola_Ivan_2013_MTS", "2015-image-coll", "2017-kouril-illcell", "2019-ic", "AA2020", "aigner-2025-vhv", "alacruzECR2004", "alacruzVisSym2004", "Alharbi_2021", "Alsallakh_2012_RCW", "alsallakh-2009-iva", "altendorfer-2008-vts", "amabili2021", "amirkhanov_2012", "amirkhanov_2012heur", "amirkhanov-2011", "amirkhanov-2014-ama", "amirkhanov-2018-withteeth", "amirkhanov-2021-diss", "amirkhanov2010AMA", "ammer-2008-lst", "An-2018-CHI", "AndreUrsits_2013_APP", "antes-2023-vhv", "Antonini2020", "arbesser_ba_2023", "Artner-2004-Hig", "Artner-2005-Hig", "Artner-2005-Thesis", "audioflow", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2007-ant", "balabanian-2008-hvv", "Balabanian-2010-IIV", "Balabanian-2010-PSDSV", "Balduz_01", "Basch_2011_ATA", "Bauer_David_2018", "Bauer_Johannes_2014_IWB", "bauer-2015-cbfd", "Bayat_2019", "bayer-2015-gapaot", "bbeer-2013", "Bechtold2020", "Beer_2011_DVE", "Beham_Michael_PSC", "beham-2014-cupid", "bernhard-2006-dvrcta", "bernhard-2014-GTOM", "bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-thesis", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" }, { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/waldin-2019-ccm/", "__class": "Publication" }, { "id": "steinboeck-2018-lbg", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Casual Visual Exploration of Large Bipartite Graphs Using Hierarchical Aggregation and Filtering", "date": "2018-10", "abstract": "Bipartite graphs are typically visualized using linked\nlists or matrices. However, these classic visualization techniques\ndo not scale well with the number of nodes. Biclustering has\nbeen used to aggregate edges, but not to create linked lists\nwith thousands of nodes. In this paper, we present a new\ncasual exploration interface for large, weighted bipartite graphs,\nwhich allows for multi-scale exploration through hierarchical\naggregation of nodes and edges using biclustering in linked\nlists. We demonstrate the usefulness of the technique using two\ndata sets: a database of media advertising expenses of public\nauthorities and author-keyword co-occurrences from the IEEE\nVisualization Publication collection. Through an insight-based\nstudy with lay users, we show that the biclustering interface leads\nto longer exploration times, more insights, and more unexpected\nfindings than a baseline interface using only filtering. However,\nusers also perceive the biclustering interface as more complex.", "authors_et_al": false, "substitute": null, "main_image": { "description": "BiCFlows showing visualization authors and their key words", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 708, "image_height": 702, "name": "steinboeck-2018-lbg-teaser.png", "type": "image/png", "size": 137906, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1378, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Daniel", "lastname": "Steinböck", "searchname": "Daniel Steinböck", "is_teacher": false, "name": "Daniel Steinböck", "publications": [ "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "waldner-2020-tbg" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1378", "__class": "Author" }, { "id": 166, "drupal_id": 111, "orcid_id": "0000-0002-8569-4149", "website": "https://www.cg.tuwien.ac.at/staff/EduardGröller", "firstname": "Eduard", "lastname": "Gröller", "searchname": "Eduard Gröller", "is_teacher": true, "name": "Eduard Gröller", "publications": [ "a-2001-ah", "a-2001-ahr", "a-2001-Rec", "Acquisto-1995-DCR", "alacruzECR2004", "Alharbi_2021", "Alsallakh_2012_RCW", "amabili-2024-smg", "amabili-2025-lpb", "amirkhanov_2012", "amirkhanov_2012heur", "amirkhanov_2013_AMA", "amirkhanov-2011", "amirkhanov-2014-ama", "amirkhanov-2017-cffet", "amirkhanov-2018-withteeth", "amirkhanov-2019-manylands", "amirkhanov2010AMA", "amirkhanov2020visual", "Antonini2020", "Artner-2004-Hig", "Artner-2005-Hig", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2007-ant", "balabanian-2008-hvv", "balabanian-2008-tst", "Balabanian-2010-IIV", "Balabanian-2010-PSDSV", "Bartroli-2002-Pro", "Bartroli-2003-Geo", "Bartroli-2003-Non", "beham-2014-cupid", "Berger_2011_UAE", "Berger-1998-Col", "Berger-2000-Col", "Bruckner_Stefan_2015_VAS", "Bruckner-2003-The", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "burns-2007-fea", "Carata_2011_IVE", "Castro-1998-TFS", "Cornel_Daniel_2015_VOC", "cornel-2022-wih", "coto-2005-MAC", "Csebfalvi-1999-Fast", "Csebfalvi-1999-MIP", "Csebfalvi-2000-Fas", "Csebfalvi-2000-GELR", "Csebfalvi-2000-Gra", "Csebfalvi-2000-IVRBM", "Csebfalvi-2001-Fas", "Csebfalvi-2001-Int", "Csebfalvi-2001-NPVR", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "dataset-stagbeetle", "Dhanoa_2021", "dhanoa-2022-apm", "dhanoa-2025-dsg", "Diehl_2015", "Diehl_2021", "diehl-2013-VTA", "Diehl-2017-Albero", "Edi_2019_10_01", "eg-tut2005-iv", "eichner-2022-music", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "fernandez-2022-ergoexplorer", "fischel-1995-vis", "Fischel-1995-VLS", "Fischel-1997-CSVVP", "Fischel-1998-Cas", "fmistelbauer-2014-adict", "fritz-2009-ava", "Froehler_Berhnard_2015_ESM", "fuchs_vhml", "fuchs-2008-del", "Fuhr-1999-Multi", "Fuhrmann-1998-Rea", "fuhrmann-1998-reaX", "Furmanova_2018", "furmanova_2020", "furmanova-2024-bvp", "gall-2025-marv", "Gall2021", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu-2010-CIE", "Gillmann2021", "Glaeser-1997-EVG", "Glaeser-1998-AFa", "Glaeser-1998-Eff", "Glaeser-1999-Fas", "glanznig-2009-LAMC", "Grimm-2003-CPU", "Grimm-2003-HYP", "grimm-2004-arefined", "GRIMM-2004-FDM", "GRIMM-2004-FDMX", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I1", "Groeller_2016_I2", "Groeller_2016_I3", "Groeller_2016_I4", "Groeller_2016_I5", "Groeller_2016_I6", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P3", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-1987-opt", "groeller-1988-duale", "groeller-1988-using", "groeller-1991-frac", "groeller-1991-temp", "groeller-1992-frac", "groeller-1992-fractals", "groeller-1992-fraktale", "groeller-1992-vis", "groeller-1993-acc", "groeller-1993-cha", "groeller-1993-coh", "groeller-1993-coh1", "groeller-1993-daten", "groeller-1993-dist", "groeller-1993-disto", "groeller-1993-eff", "groeller-1993-exp", "groeller-1993-frak", "groeller-1993-grund", "groeller-1993-oct", "Groeller-1994-AIV", "groeller-1994-appl", "Groeller-1994-AVT", "groeller-1994-coh", "Groeller-1994-CSA", "groeller-1994-ext", "Groeller-1994-HIS", "groeller-1994-hyper", "Groeller-1994-IDN", "groeller-1994-int", "Groeller-1994-ITV", "groeller-1994-mod", "Groeller-1994-MRN", "groeller-1994-tech", "groeller-1994-vis", "groeller-1995-appl", "groeller-1995-attract", "Groeller-1995-CCG", "groeller-1995-dyn", "Groeller-1995-GOW", "Groeller-1995-IED", "groeller-1995-inter", "groeller-1995-lok", "groeller-1995-mod", "groeller-1995-non", "Groeller-1995-SDC", "groeller-1995-vis", "groeller-1996-ACE", "groeller-1996-avn", "Groeller-1998-TunVis", "Groeller-1998-VAD", "Groeller-1998-VDS", "Groeller-1999-Dat", "Groeller-1999-Vis", "Groeller-2000-Vis", "Groeller-2001-IDV", "groeller-2002-anw", "Groeller-2002-Ins", "groeller-2002-med", "groeller-2002-wiss", "groeller-2003-fea", "groeller-2003-med", "groeller-2003-scie", "groeller-2003-vol", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-abs", "groeller-2005-dia", "groeller-2005-foc", "groeller-2005-imp", "groeller-2005-impo", "groeller-2005-proc", "groeller-2005-sma", "groeller-2005-smar", "groeller-2005-volg", "groeller-2006-cgf", "groeller-2006-civ", "groeller-2006-ivt", "groeller-2006-tvcg", "groeller-2007-csu", "groeller-2007-fci", "groeller-2007-mva", "groeller-2007-vis", "groeller-2007-visII", "groeller-2007-vws", "groeller-2008-att2", "groeller-2008-How", "groeller-2008-hsp", "groeller-2008-irs", "groeller-2008-kav2", "groeller-2008-kav3", "groeller-2008-kav4", "groeller-2008-lsy", "Groeller-2010-CVC", "groeller-2010-How", "Groeller-2010-IFI", "Groeller-2010-IVVI", "groeller-2010-PTT", "Groeller-2011-HSH", "Groeller-2017-AMOS", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "groeller-2022-iva", "groeller-2022-iva-1", "groeller-2022-ivd", "groeller-2023-ivd", "groeller-2023-siv", "groeller-2023-siv-1", "groeller-2023-siv-2", "groeller-2023-tfo", "groeller-2024-cui", "groeller-cvc-2009", "groeller-scv-2009", "groeller-svo-2009", "Groeller-thesis", "groeller-vks-2009", "Groller_2021-12-03", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "grossmann-2022-conceptSplatters", "Hadwiger-2003-MIP", "haidacher_2010_statTF", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-VAM", "Halladjian_2020", "Hauser-1999-SSI", "Hauser-2000-Tho", "Hauser-2000-Two", "Hauser-2000-TwoX", "Hauser-2002-SSI", "Hauser-2003-Acc", "Heim_2021", "heim-2024-accustripes", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2007-sem", "heinzl-2008-sam", "Herghelegiu-2012-BPV", "herzberger-2024-roh", "Hladuvka-2000-Cur", "Hladuvka-2000-Exp", "Hladuvka-2000-ExpX", "Hladuvka-2001-DDI", "Hladuvka-2001-Dir", "Hladuvka-2001-Exp", "Hladuvka-2001-Sal", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hladuvka-2002-Sma", "Jankovic-1994-PBM", "jankovic-1994-phy", "jankovic-1995-heter", "Kanitsar-2001-Aut", "Kanitsar-2001-Per", "Kanitsar-2001-Pos", "Kanitsar-2001-PVI", "kanitsar-2002-Chr", "Kanitsar-2002-CPR", "kanitsar-2002-CPRX", "Kanitsar-2003-ADV", "Kanitsar-2003-Adva", "Kanitsar-2003-CTa", "Kanitsar-2003-Dem", 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Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "NetVis", "drupal_id": 9472, "name": "Network Visualization", "short_abstract": "This research area aims to investigate how to best visualize graph-typed data, both abstractly as well as in domain-specific applications.", "abstract": "Graph-typed data is ubiquitous, from logistical or transportation networks, through social or correspondence networks, all the way to more abstract biochemical interaction networks. Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "information visualization", "bipartite graphs", "biclustering", "insight-based evaluation" ], "weblinks": [ { "href": "https://users.cg.tuwien.ac.at/~waldner/bicflows/", "caption": "BiCFlows online", "description": "BiCFlows online for exploring Austria's media transparency database and the IEEE Visualization paper authors and their key words. ", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "steinboeck-2018-lbg-paper.pdf", "type": "application/pdf", "size": 1871535, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-paper:thumb{{size}}.png" }, { "description": "BiCFlows showing visualization authors and their key words", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 708, "image_height": 702, "name": "steinboeck-2018-lbg-teaser.png", "type": "image/png", "size": 137906, "path": "Publication:steinboeck-2018-lbg", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/steinboeck-2018-lbg-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/", "__class": "Publication" }, { "id": "mazurek-2018-veq", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Visualizing Expanded Query Results", "date": "2018-06", "abstract": "When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines\ntherefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches.\nHowever, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe\nthe adoption of set-based text visualization techniques to visualize how query expansions enrich the result space of a given\nuser query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic\nmodeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list\nof query expansion suggestions to three set-based text visualization techniques adopted for visualizing expanded query results\n– namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive\nquery expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall.\nOverall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive\nrecommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative\nuse cases of set-based text visualization techniques in the context of web search.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Density-based compact Euler Diagram", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 919, "image_height": 656, "name": "mazurek-2018-veq-teaser.png", "type": "image/png", "size": 34882, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1364, 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", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "Information visualization", "search interfaces", "empirical studies in visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "mazurek-2018-veq-paper.pdf", "type": "application/pdf", "size": 1891501, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-paper:thumb{{size}}.png" }, { "description": "Density-based compact Euler Diagram", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 919, "image_height": 656, "name": "mazurek-2018-veq-teaser.png", "type": "image/png", "size": 34882, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-teaser:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "name": "mazurek-2018-veq-video.mp4", "type": "video/mp4", "size": 30231176, "path": "Publication:mazurek-2018-veq", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/mazurek-2018-veq-video:video.mp4" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/mazurek-2018-veq/", "__class": "Publication" }, { "id": "waldner-2018-ved", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Visual Data Exploration and Analysis in Emerging Display Environments ", "date": "2018-04-05", "abstract": "Increasingly powerful computing and display hardware open up entirely new ways for visual data exploration and analysis. Powerful machines and emerging display environments facilitate novel visual exploration techniques, collaborative data analysis, and even immersion into the scientific data. This talk will address the challenges we faced when bringing biomolecular visual analysis tools and complex molecular visualizations into such large, multi-user environments. A special focus lies on interfaces and attention guidance techniques we designed and evaluated to keep the user oriented and reduce visual clutter. ", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "geymayer-2017-std", "grammatikaki-2025-htr", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "heim-2024-accustripes", "indirectBiasLanguageModels-2023", "komon-2025-dco", "komon-2025-webigeo", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "matt-2024-cvil", "matt-2025-scv", "mazurek-2018-veq", "pahr-2024-ieo", "polatsek-2018-stv", "reina-2020-mtv", 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"url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1110", "__class": "Author" } ], "event": "Emerging Technologies in Scientific Data Visualisation - CECAM", "location": "Pisa, Italy", "research_areas": [], "keywords": [], "weblinks": [ { "href": "https://www.cecam.org/workshop-1586.html", "caption": "CECAM workshop", "description": null, "main_file": 0 } ], "files": [], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/waldner-2018-ved/", "__class": "Publication" }, { "id": "polatsek-2018-stv", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Exploring visual attention and saliency modeling for task-based visual analysis", "date": "2018-02", "abstract": "Memory, visual attention and perception play a critical role in the design of visualizations. The way users observe a visualization is affected by salient stimuli in a scene as well as by domain knowledge, interest, and the task. While recent saliency models manage to predict the users’ visual attention in visualizations during exploratory analysis, there is little evidence how much influence bottom-up saliency has on task-based visual analysis. Therefore, we performed an eye-tracking study with 47 users to determine the users’ path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database [1]. We also compared our task-based eye tracking data to the data from the original memorability experiment by Borkin et al. [2]. We found that solving a task leads to more consistent viewing patterns compared to exploratory visual analysis. However, bottom-up saliency of a visualization has negligible influence on users’ fixations and task efficiency when performing a low-level analytical task. Also, the efficiency of visual search for an extreme target data point is barely influenced by the target’s bottom-up saliency. Therefore, we conclude that bottom-up saliency models tailored towards information visualization are not suitable for predicting visual attention when performing task-based visual analysis. We discuss potential reasons and suggest extensions to visual attention models to better account for task-based visual analysis.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "graphical abstract", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 591, "image_height": 584, "name": "polatsek-2018-stv-graphical abstract.png", "type": "image/png", "size": 193042, "path": "Publication:polatsek-2018-stv", "url": "https://www.cg.tuwien.ac.at/research/publications/2018/polatsek-2018-stv/polatsek-2018-stv-graphical abstract.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2018/polatsek-2018-stv/polatsek-2018-stv-graphical abstract:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1545, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Patrik", "lastname": 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"https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=171", "__class": "Author" }, { "id": 1546, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Peter", "lastname": "Kapec", "searchname": "Peter Kapec", "is_teacher": false, "name": "Peter Kapec", "publications": [ "polatsek-2018-stv" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1546", "__class": "Author" }, { "id": 1547, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Wanda", "lastname": "Benesova", "searchname": "Wanda Benesova", "is_teacher": false, "name": "Wanda Benesova", "publications": [ "polatsek-2018-stv" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1547", "__class": "Author" } ], "doi": "https://doi.org/10.1016/j.cag.2018.01.010", "journal": "Computers & Graphics", "number": "2", "open_access": "no", "research_areas": [ { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning. ", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" }, { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2018/polatsek-2018-stv/", "__class": "Publication" }, { "id": "Waldner_2017_11", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Guiding Attention in Complex Visualizations using Flicker", "date": "2017-11-17", "abstract": "Drawing the user’s gaze to an important item in an image or a graphical user interface is a common challenge. Usually, some form of highlighting is used, such as a clearly distinct color or a border around the item. Flicker is also a strong visual attractor in the entire visual field, without distorting, suppressing, or adding any scene elements. While it is very salient, it is often perceived as annoying. In this talk, I will present our research on how flicker can be used as attention guidance technique in cluttered visualizations while lowering its negative side-effects. In particular, I will first present results of studies examining a two-stage flicker technique for dynamic visualizations on large displays. Then, I will present we our explorations of high frequency flicker (60 to 72 Hz) to guide the user’s attention in images. At such high frequencies, the critical flicker frequency (CFF) threshold is reached, which makes the flicker appear to fuse into a stable signal. However, the CFF is not uniform across the visual field, but is higher in the peripheral vision at normal lighting conditions. We show that high frequency flicker, using personalized attributes like patch size and luminance, can be easily detected by observers in the peripheral vision, but the signal is hardly visible in the foveal vision when users directly look at the flickering patch. We demonstrate that this property can be used to draw the user’s attention to important image regions using a standard high refresh-rate computer monitor with minimal visible modifications to the image.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "geymayer-2017-std", "grammatikaki-2025-htr", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "heim-2024-accustripes", "indirectBiasLanguageModels-2023", "komon-2025-dco", "komon-2025-webigeo", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "matt-2024-cvil", "matt-2025-scv", "mazurek-2018-veq", "pahr-2024-ieo", "polatsek-2018-stv", "reina-2020-mtv", "sietzen-2021-perturber", "sietzen-ifv-2019", "sMolBoxes_2022", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "tekaya-2025-amo", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "webGPU_aggregateVis-2023", "wu-2021", "wu-2021-vi" ], "publications_first": [], "project_leader": [ "d9282", "d9522", "d9555", "deskollage" ], "publication_field": [ { "publ_id": "arbesser_ba_2023", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bayat-2022-mva", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "boesch_2014_browserHistoryVis", 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Illustrators apply such techniques, for instance, to present non-important parts of an organ in a simplified way, while using detailed representations to put the focus of attention on a region where a tumor might be situated.\n\nTo identify such meaningful visual abstractions for visualization, their effectiveness for human perception and cognition has to be assessed, and they have to be formalized in order to make them applicable in rendering algorithms. In this project, we try to advance the understanding of procedural visual abstraction of complex physiological processes that describe the complex machinery of biological life.", "website": "https://www.cg.tuwien.ac.at/research/area/IllVis", "teaser": { "name": "teaser.jpg", "path": "research_area:IllVis", "type": "image/jpeg", "size": 34861, "orig_name": "teaser.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/IllVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "2017-kouril-illcell", "Alharbi_2021", "antes-2026-maa", "Antonini2020", "Balabanian-2010-IIV", "Balduz_01", "Bayat_2019", "Bechtold2020", "Birkeland-2012-IMC", "brandstaetter_01", "bruckner-2005-ICV", "bruckner-2006-ICE", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2010-HVC", "bruckner-2010-IFC", "cellVIEW_2015", "Chen-Information-2016", "Deutsch-2018", "Dhanoa_2021", "Dhanoa2024", "dietrich-2016-viseq2", 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"Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", "mindek_peter-2014-cs_kaust", "mindek-2013-cs_cvut", "mindek-2013-csl", "mindek-2013-pel", "mindek-2014-mcs", "mindek-2014-vivi_cvut", "mindek-2015-mc", "mindek-2015-mctalk", "mindek-2016-utah-talk", "mindek-2017-dsn", "mindek-2017-marion", "mindek-2017-virtualcell", "mindek-2018-fyi", "mindek-2019-mci", "mindek-thesis", "mistelbauer_2013_SIV", "Mistelbauer_2021", "Mistelbauer_Gabriel_2015_NHR", "Mistelbauer_Gabriel_2016", "mistelbauer-2010-pvv", "mistelbauer-2012-cr", "mistelbauer-2012-ssv", "mistelbauer-2013-cfa", "mistelbauer-2015-abvv", "Mitterhofer_2017", "mizuno-2019-eurovis", "Mlejnek_2004_PF", "Mlejnek_2005_AOEPF", "Mlejnek_2006_MVOA", "Mlejnek-2004-Int", "Mlejnek-2004-ITVAC", "Mlinaric-2015-MT", "Moellinger_Christian_IDE2", "moerth_eric-2016-3DF", "moerth-2018-tpose", "monitzer_2008_FluSi", "Morar_2012_ISB", "Muehlbacher_diss_2018", "mueller-2009-ivs", "Muigg_2011_ISI", "Muigg_2011_VC", "Muigg-2008-four", "muigg-2012-svr", "Musleh_PhD", "musleh-2024-conan", "Neumann-2002-Fea", "nguyen_2020-covid", "Niedermayer_2018", "Novotny_2013_ASVD", "Nowak_2020", "Nowak_2021", "Oancea_2018_1", "Oancea_Stefan_2016_VOT", "Oancea2018OAK", "oeltze-2013-tut", "opitz-2009-cvv", "Ortner_PhD", "Ortner2020", "Pahr_PhD", "pahr-2021-vologram", "pahr-2024-ieo", "pahr-2024-squishicalization", "pahr-2025-holographs", "pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" }, { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/Waldner_2017_11/", "__class": "Publication" }, { "id": "geymayer-2017-std", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "How Sensemaking Tools Influence Display Space Usage", "date": "2017-06", "abstract": "We explore how the availability of a sensemaking tool influences users’ knowledge externalization strategies. On a large display,\nusers were asked to solve an intelligence analysis task with or without a bidirectionally linked concept-graph (BLC) to organize\ninsights into concepts (nodes) and relations (edges). In BLC, both nodes and edges maintain “deep links” to the exact source\nphrases and sections in associated documents. In our control condition, we were able to reproduce previously described spatial\norganization behaviors using document windows on the large display. When using BLC, however, we found that analysts apply\nspatial organization to BLC nodes instead, use significantly less display space and have significantly fewer open windows.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 805, "image_height": 344, "name": "geymayer-2017-std-image.PNG", "type": "image/png", "size": 604999, "path": "Publication:geymayer-2017-std", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1477, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Thomas", "lastname": "Geymayer", 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"publ_id": "steinboeck-2017-vefp", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "steinboeck-2018-lbg", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "stoff-2025-pvu", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "stoff-concepMap-2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "textures-3d-printing", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "trautner-2018-imd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "unger-2019_vcp", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Unger2020", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "waldner-2013-ubiWM", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2014-af", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2014-ghi", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2017-vph", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2019-rld", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": 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"name": "Dieter Schmalstieg", "publications": [ "Bruckner-2003-The", "Dorf-2001-Fin", "Encarnacao-1999-ATr", "Encarnacao-1999-CAD", "Encarnacao-1999-The", "Faisst-2000-VPRR", "Faisstnauer-1997-DINIVE", "Faisstnauer-1998-Com", "Faisstnauer-1998-Dev", "Faisstnauer-1999-Pri", "Faisstnauer-2000-Pri", "Faisstnauer-2000-Prio", "Faisstnauer-2000-Sch", "Faisstnauer-2001-Sch", "Fuhr-1999-Cal", "Fuhr-1999-Conc", "Fuhr-1999-ConcX", "fuhrmann-1997-CAR", "Fuhrmann-1998-Col", "Fuhrmann-1998-CVA", "Fuhrmann-1998-Str", "Fuhrmann-1999-Fas", "Fuhrmann-2000-Pra", "gervautz-1994-int", "gervautz-1995-comp", "gervautz-1996-SMU", "Gervautz-1999-Vir", "geymayer-2017-std", "Hes-1999-", "Hesina-1998-ANe", "Hesina-1998-ANEX", "Hesina-1999-Dis", "Kauf-2000-Con", "Mantler-1998-Dyn", "Mantler-1998-DynX", "Mazuryk-1995-ZRA", "Purg2015-China", "Schaufler-1996-HFI", "Schm-2000-Bri", "Schm-2000-BriX", "Schm-2000-Stb", "schmalstieg-1994-prog", "Schmalstieg-1995-CHI", "Schmalstieg-1995-IGC", "Schmalstieg-1995-OCD", 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"Dorfmueller-Ulhaas-thesis", "field_id": "supervisor", "ordernum": 1 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=202", "__class": "Author" } ], "booktitle": "EuroVis Workshop on Visual Analytics", "event": "EuroVis 2017", "lecturer": [ { "id": 1477, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Thomas", "lastname": "Geymayer", "searchname": "Thomas Geymayer", "is_teacher": false, "name": "Thomas Geymayer", "publications": [ "geymayer-2017-std", "waldner-2021-leo" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "geymayer-2017-std", "field_id": "lecturer", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1477", "__class": "Author" } ], "research_areas": [ { "id": "InfoVis", "drupal_id": 4128, "name": "Information Visualization and Visual Analytics", "short_abstract": "In this research area, our focus lies on novel visual encodings 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", "abstract": "Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
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"deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "sensemaking", "large displays", "evaluation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 805, "image_height": 344, "name": "geymayer-2017-std-image.PNG", "type": "image/png", "size": 604999, "path": "Publication:geymayer-2017-std", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-image.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "geymayer-2017-std-paper.pdf", "type": "application/pdf", "size": 1117995, "path": "Publication:geymayer-2017-std", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/geymayer-2017-std-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/geymayer-2017-std/", "__class": "Publication" }, { "id": "waldner-2017-vph", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Exploring Visual Prominence of Multi-Channel Highlighting in Visualizations", "date": "2017-05", "abstract": "Visualizations make rich use of multiple visual channels so that there are few resources left to make selected focus elements visually\ndistinct from their surrounding context. A large variety of highlighting techniques for visualizations has been presented in the past,\nbut there has been little systematic evaluation of the design space of highlighting. We explore highlighting from the perspective\nof visual marks and channels – the basic building blocks of visualizations that are directly controlled by visualization designers.\nWe present the results from two experiments, exploring the visual prominence of highlighted marks in scatterplots: First, using\nluminance as a single highlight channel, we found that visual prominence is mainly determined by the luminance difference between\nthe focus mark and the brightest context mark. The brightness differences between context marks and the overall brightness level\nhave negligible influence. Second, multi-channel highlighting using luminance and blur leads to a good trade-off between highlight\neffectiveness and aesthetics. From the results, we derive a simple highlight model to balance highlighting across multiple visual\nchannels and focus and context marks, respectively.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 160, "image_height": 124, "name": "waldner-2017-vph-.png", "type": "image/png", "size": 20447, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "geymayer-2017-std", "grammatikaki-2025-htr", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "heim-2024-accustripes", "indirectBiasLanguageModels-2023", "komon-2025-dco", "komon-2025-webigeo", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "matt-2024-cvil", "matt-2025-scv", "mazurek-2018-veq", "pahr-2024-ieo", "polatsek-2018-stv", "reina-2020-mtv", "sietzen-2021-perturber", "sietzen-ifv-2019", "sMolBoxes_2022", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "tekaya-2025-amo", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "webGPU_aggregateVis-2023", "wu-2021", "wu-2021-vi" ], "publications_first": [], "project_leader": [ "d9282", "d9522", "d9555", "deskollage" ], "publication_field": [ { "publ_id": "arbesser_ba_2023", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bayat-2022-mva", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "boesch_2014_browserHistoryVis", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "breitenecker-2020-bct", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bugnar-esk-2021", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "casellato-2016-pkg", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "cizmic-2018-evd", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "da_cunha_melo-2023-c3m", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "donabauer-2015-asc", "field_id": "supervisor", "ordernum": 0 }, { 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"publ_id": "Unger2020", "field_id": "supervisor", "ordernum": 1 }, { "publ_id": "waldner-2013-ubiWM", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2014-af", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2014-ghi", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2017-vph", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "waldner-2019-rld", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "webGPU_aggregateVis-2023", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "wolf-2023-cc", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "wolf-2024-jhd", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1110", "__class": "Author" }, { "id": 925, "drupal_id": 308, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/AlexeyKarimov", "firstname": "Alexey", "lastname": "Karimov", "searchname": "Alexey Karimov", "is_teacher": false, "name": 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By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
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", "filetitle": "supplemental material", "main_file": false, "use_in_gallery": false, "access": "public", "name": "waldner-2017-vph-supplemental material.pdf", "type": "application/pdf", "size": 1396255, "path": "Publication:waldner-2017-vph", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-supplemental material.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/waldner-2017-vph-supplemental material:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/waldner-2017-vph/", "__class": "Publication" }, { "id": "Waldin_Nicholas_2017_FlickerObserver", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Flicker Observer Effect: Guiding Attention Through High Frequency Flicker in Images", "date": "2017-05", "abstract": "Drawing the user's gaze to an important item in an image or a graphical user interface is a common challenge. Usually, some form of highlighting is used, such as a clearly distinct color or a border around the item. Flicker can also be very salient, but is often perceived as annoying. In this paper, we explore high frequency flicker (60 to 72 Hz) to guide the user's attention in an image. At such high frequencies, the critical flicker frequency (CFF) threshold is reached, which makes the flicker appear to fuse into a stable signal. However, the CFF is not uniform across the visual field, but is higher in the peripheral vision at normal lighting conditions. Through experiments, we show that high frequency flicker can be easily detected by observers in the peripheral vision, but the signal is hardly visible in the foveal vision when users directly look at the flickering patch. We demonstrate that this property can be used to draw the user's attention to important image regions using a standard high refresh-rate computer monitor with minimal visible modifications to the image. In an uncalibrated visual search task, users could in a crowded image easily spot the specified search targets flickering with very high frequency. They also reported that high frequency flicker was distracting when they had to attend to another region, while it was hardly noticeable when looking at the flickering region itself.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 1461, "drupal_id": 1003, "orcid_id": null, "website": "https://www.cg.tuwien.ac.at/staff/NicholasWaldin", "firstname": "Nicholas", "lastname": "Waldin", "searchname": "Nicholas Waldin", "is_teacher": false, "name": "Nicholas Waldin", "publications": [ "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Colormaps", "Waldin_Nicholas_2016_Individualization", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis" ], "publications_first": [], "project_leader": [], "publication_field": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1461", "__class": "Author" }, { "id": 1110, "drupal_id": 625, "orcid_id": "0000-0003-1387-5132", "website": "https://www.cg.tuwien.ac.at/staff/ManuelaWaldner", "firstname": "Manuela", "lastname": "Waldner", "searchname": "Manuela Waldner", "is_teacher": true, "name": "Manuela Waldner", "publications": [ "2019-ic", "aigner-2025-vhv", "Alharbi_2021", "bayat-2024-awt", "bernhard-2016-gft", "boffi-2024-bagginghook", "byska-2019-mdfc", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "geymayer-2017-std", "grammatikaki-2025-htr", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "heim-2024-accustripes", "indirectBiasLanguageModels-2023", "komon-2025-dco", "komon-2025-webigeo", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "matt-2024-cvil", "matt-2025-scv", "mazurek-2018-veq", "pahr-2024-ieo", "polatsek-2018-stv", "reina-2020-mtv", "sietzen-2021-perturber", "sietzen-ifv-2019", "sMolBoxes_2022", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "tekaya-2025-amo", "Waldin_Nicholas_2016_Chameleon", 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"pahr-2025-nodkant", "pahr-thesis", "Pahr2020", "Pajer_2012_VoM", "Panayotov", "papantonakis-2024-rmf", "Parzer_2013_IIG", "patel_2009_MC", "patel_daniel_2007_IRSD", "patel-2008-tsa", "patel-2009-evr", "patel-2011-PEA", "Pauschenwein_2022", "Peter_2012_AIV", "Pezenka-2016-MT", "Pfahler_David_2015_IPI", "Pfahler-2016-MT", "PH-2011-LDS", "phd-viola", "Philipp_Hochhauser_2023", "Pilizar_-2020-bachelor", "piringer-2009-hds", "Plank_Pascal_2015_HVP", "plank-2017-sldg", "PMI_AR_2012", "pointner_simon-2020", "polatsek-2018-stv", "polatsek-2018-stv", "Pramerdorfer-2011-PAV", "preim-2009-sve", "Presch_2020", "Priselac2021", "puehringer-2009-sbm", "puenguentzky-2014-ht", "Purchase-2020-gd", "raidou_2018_bladderrunner", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_2020Onc", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_RO2019", "raidou_shonan167", "raidou_slicedice", "raidou_star2019", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2018_rv3", "raidou2018visualflatter", "raidou2019_prsps", "rasch-2016-imgses", "Rau08", "Rautek_Peter_2014_TUC", "Rautek_Peter_2014_VSA", "rautek-2005-dvr", "Rautek-2007-O3D", "Rautek-2007-SLI", "Rautek-2008-IDS", "Rautek-2008-kav", "Rautek-2008-VF", "rautek-2009-vmv", "RAUTEK06", "Rautek06Vis", "Rautek06VMLS", "rautek2010", "Red_Andreas_2015_FFT", "Reh_Andreas_VoP", "reh-2013", "reina-2020-mtv", "Reisacher_Matthias_CPW", "Reisacher2016", "Reiter_2009_IXIA", "Reiter2017CVAP", "Rezk06Vis", "Ribicic_2012_SUS", "Ribicic_2012_VAS", "Ribicic-2010-CRS", "Riegelnegg_2019", "rinortner_susanne-2019-vpicc", "rinortner_susanne-2019-vpicc", "Rippberger_2019", "rippl-2023-atg", "ritzberger-2010-nar", "Ropinski-2012-UBT", "rraidou_clinical", "rraidou_EG17", "rraidou_phdbook", "rsabin-2013", "ruiz-2008-OVR", "ruiz-2008-SEV", "samoul-2019-cnp", "Sanjin_Rados_RV", "Sarkis_2021", "Sbardellati-2019-vcbm", "Sbardellati-2019-vcbm", "sbardellati-2021-eveos", "scharsach-2005-adv", "Schernthaner-2017-MCP", "schindler_2020vis", "Schindler-2022-eurovis", "schindler2020", "schleicher-2011-VLSC", "Schmidlehner_Sandra_SbC", "schmidlehner2021", "schmidt-2011-mt", "schmidt-phd", "schmidt-phd", "schoellhuber-2008-asc", "scholz_2021", "Schrempf_2018", "Schrempf_2020", "SCHUETZ-2024-SIMLOD", "schuetz-2025-splatshop", "schuh-2018-mesh-blending", "schultes_lampi", "Schulze_Florian_2013_CMI", "schwankl-2013-smis", "seyfert-2017", "shakhova-2023-atp", "sietzen-2019-wnn", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sifuentes-2018-crowd", "SifuentesGangl2017FIS", "sikachev_peter_2010_STLCI", "sikachev_peter-2011-dfc", "sikachev_peter-2011-incshad", "sikachev_peter-2011-metavis", "sikachev_peter-2011-protovis", "sikachev-2010-DFC", "sikachev-2010-ill_vis_vol_ren", "Sippl_Sebastian_EFG", "smiech-2018-tei", "Smisek_Michal_A3D", "Sollboeck_2011_FSS", "sollboeck-2011", "Solteszova-2009-VQN", "solteszova-2010-MOS", "Solteszova2016", "Sorger_2013_nMI", "sorger_2017_metamorphers", "sorger-2016-fowardabstraction", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "Soros_AVN_2011", "Soros-AV-2010", "Spechtenhauser_Florian_2016", "spegel-gruenberger-2020", "Spelitz2020", "spoerk-2010-hpgpu", "Stabel_2012_UDM", "stappen_2017_animatedcomic", "Staudacher_2022", "staudacher_florian-2016-topo3d", "stehno-2011-RVD", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "StickmanMax-shoetta-kampfer", "stoff-2025-pvu", "stoff-concepMap-2021", "StrakaVis2004", "stritzel2022", "Strohmayer-2018-BT", "sturl_2016_dynlabels", "suntinger-2008-etet", "suntinger-2008-tet", "Swoboda_Nicolas_VIT", "Swoboda2020", "szabo_patrik-2022-tour", "szabo-thesis", "takahashi-2019-acdt", "tekaya-2025-amo", "tekaya-2025-amo", "termeer-2006-000", "termeer-2007-covicad", "termeer-2008-scmr", "termeer-2008-vis", "termeer-2009-cvc", "termeer-2009-scmr", "textures-3d-printing", "thesis-kronsteiner", "thesis-tekaya", "Thomas-2018", "Thurner-2005-IVR", "toth-2007-ndd", "TR-186-2-05-01", "TR-186-2-05-02", "TR-186-2-05-03", "TR-186-2-05-04", "TR-186-2-05-06", "TR-186-2-05-07", "TR-186-2-05-08", "TR-186-2-06-01", "TR-186-2-06-02", "TR-186-2-06-03", "TR-186-2-06-04", "TR-186-2-07-02", "TR-186-2-07-03", "TR-186-2-07-04", "TR-186-2-07-05", "TR-186-2-07-06", "TR-186-2-07-07", "TR-186-2-07-08", "TR-186-2-07-10", "TR-186-2-07-11", "TR-186-2-07-12", "TR-186-2-08-01", "TR-186-2-08-02", "TR-186-2-08-03", "TR-186-2-08-04", "TR-186-2-08-05", "TR-186-2-08-06", "TR-186-2-08-07", "TR-186-2-08-08", "TR-186-2-08-10", "TR-186-2-08-11", "TR-186-2-08-12", "TR-186-2-08-14", "TR-186-2-09-01", "TR-186-2-15-03", "TR1862162", "Tramberger_2018", "Tramberger2017BCS", "Trautner_Thomas_2014_VAE", "trautner-2018-imd", "trautner-2018-imd", "travnicek-2017-thm", "Trenkwalder-2004-MAR", "tretyak-2025-tacmedvr", "Troidl_2019", "Troidl_2021", "Troidl_2022", "turner-aso", "tut-eg-2006", "tut-siggraph-2006", "tut-vis-2006", "tut-vis-2007", "tweebo-2014", "ulschmid-2024-reo", "ulschmid-2025-apc", "unger_2017", "unger-2019_vcp", "Unger2020", "unterguggenberger-2024-fropo", "usul-thesis", "vad_2015_phd", "Vad_Viktor_2015_RVV", "vad-2016-bre", "vaico", "varchola_andrej-2012-fetoscopic", "Vasileva_2022", "Vasileva-2018", "vessel2003", "Viola_2013_IDV", "Viola_Ivan_2013_CAI", "Viola_Ivan_2013_D3D", "Viola_Ivan_2013_DC", "Viola_Ivan_2013_GS", "Viola_Ivan_2013_HQ3", "Viola_Ivan_2013_RMA", "Viola_Ivan_2013_RSb", "Viola_Ivan_2013_SVA", "Viola_Ivan_2013_VCA", "Viola_Ivan_2015_AAM", "Viola_Ivan_2015_MCT", "Viola_Ivan_2015_VBS", "Viola_Ivan_CLD", "Viola_Ivan_DAC", "Viola_Ivan_IIP", "Viola_Ivan_UVP", "Viola_Ivan_VDP", "Viola-05-Smart", "Viola-2003-GPU", "Viola-2003-Har", "viola-2003-hbn", "Viola-2003-NON", "viola-2004-gbf", "Viola-2004-GPU", "viola-2004-har", "viola-2004-har2", "viola-2004-idv", "viola-2004-imp", "Viola-2004-ImpX", "Viola-2004-ImpX2", "viola-2005-imp", "viola-2006-FoA", "viola-evr", "Viola-Pondering-2017", "viola-popular-article-2006", "Viola-vistutillustrativevis", "vis-foa", "VisWeek-Tutorial-2012-Uncertainty", "vitruvian_2019", "voglreiter-2023-tro", "Vucenovic_2022", "vucini_2006", "vucini_2008_rnp", "vucini_2009", "vucini_erald-2007-FRI", "vucini-2009-phd", "vucini-2011", "Wagner_032017", "walchhofer_ar_flowmaps", "walchhofer-tso", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldin-2019-ccm", "waldin-2019-ccm", "Waldner_2017_11", "Waldner_2017_11", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-af", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "Waser-2010-WL", "webGPU_aggregateVis-2023", "weiner_2011", "Weissenboeck_2019", "Weissenboeck_2019_PhD", "weissenboeck-2014", "Wihann_2017", "wolf-2023-cc", "wolf-2024-jhd", "Woschizka_2021", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-tvcg", "wu-2021", "wu-2021-vi", "xmastree2002", "xmastree2005", "ymca", "YOGHOURDJIAN2019", "Zambal-2003-SWA", "zambal-2005-3dact", "Zaufel_2021", "zauner-2013-svkinect", "zechmeister2020", "Zeng_2021", "Zhu-2014-flowvis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=vis", "__class": "Workgroup" }, { "id": "deskollage", "workgroup_id": "vis", "drupal_id": 4198, "drupal_path": "/research/projects/deskollage", "name": "Visual Information Foraging on the Desktop", "name_de": null, "short_title": "deskollage", "website": null, "status": "active", "short_abstract": "The goal of this project is to design and develop novel interactive visualization techniques to support knowledge workers in making sense of their unstructured, dynamic information collections. ", "abstract": "Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. Initially, knowledge workers may not even know what exactly they are looking for. Only after investigating more and more information, they develop a more concrete mental picture. These “information foraging” and “sensemaking” processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. The goal of this project was to facilitate such knowledge-intensive tasks through user interfaces that combine manual organization strategies, such as piling of papers and text annotations, with powerful automatic data processing to reveal hidden relations in the collected data.\n \nThis project led to multiple novel user interface concepts that support knowledge-intensive tasks. For instance, visual query expansion is an extension to a search engine that visualizes the effects of suggested variations on an online query result to help users to refine their query terms. The information collage lets users capture information fragments from any online sources, which can then be freely recomposed into a common knowledge representation. Natural language processing helps users to identify common topics in their collage. BiCFlows helps users to discover hidden relations in very large information collections. For instance, they can explore which authors tend to contribute to similar topics. \n\nThe user interfaces served as foundation for empirical research with users from different knowledge work domains. This research provided new insights into the strengths but also the limitations of interactive visualization for information foraging and sensemaking. On the one hand, the studies show that, in some situations, interactive visualization can be less efficient than working with unprocessed text-based information. On the other hand, the studies also show that combining automatic data analysis with interactive visualization can lead to more unexpected findings, especially when exploring large data. The extent of manual information organization varies with the task: the less users know about the gathered information, the more automatic organization they expect. \n\nIn summary, this project contributed novel user interface concepts and design guidelines for user interfaces supporting information foraging and sensemaking. The techniques and study results were published at high-ranking peer-reviewed venues, and it can be expected that the findings of this project shape the way how users seek and organize their information in the future. \n\n", "start_date": "2015-12-01", "end_date": "2018-12-31", "leader_id": 1110, "logo": { "name": "logo.png", "path": "project:deskollage", "type": "image/png", "size": 331697, "orig_name": "logo.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/deskollage/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "fwf", "contract_number": "T 752-N30", "comment": null } ], "research_areas": [ "InfoVis" ], "publications": [ "2019-ic", "byska-2019-mdfc", "casellato-2016-pkg", "cizmic-2018-evd", "donabauer-2015-asc", "dworschak-2016-szcm", "edlinger-2018-vwr", "georgiev-2019-cbg", "geymayer-2017-std", "gundacker-2020-wlm", "gusenbauer-2018", "hromniak-2019-vcn", "Koszticsak-2017-ewt", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "polatsek-2018-stv", "samoul-2019-cnp", "sietzen-2019-wnn", "sietzen-ifv-2019", "smiech-2018-tei", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "trautner-2018-imd", "unger-2019_vcp", "Waldin_Nicholas_2016_Chameleon", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2019-ccm", "Waldner_2017_11", "waldner-2017-vph", "waldner-2018-ved", "waldner-2019-rld", "waldner-2020-tbg" ], "url": "https://www.cg.tuwien.ac.at/research/projects/deskollage", "__class": "Project" }, { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/Perception", "teaser": { "name": "teaser.PNG", "path": "research_area:Perception", "type": "image/png", "size": 259428, "orig_name": "teaser.PNG", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Perception/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend", "vis" ], "publications": [ "antes-2026-maa", "bernhard-2011-maicg", "bernhard-2014-EFD", "bernhard-2014-GTOM", "bernhard-2016-gft", "birkeland_aasmund_2014_pums", "cardoso-2022-rtpercept", "cardoso-thesis", "ERLER-2017-HVR", "eschner-2023-ims", "eschner-blur-2022", "georgiev-2019-cbg", "Groeller_2016_P7", "grossmann-2021-layout", "hecher-2012-MH", "hecher-2014-MH", "HECHER-2017-HDY", "huerbe-2019-BA", "Kathi-2018-VRB", "koch-2019-PR", "Koszticsak-2017-ewt", "kraehsmaier-dsc", "KREUZER-2017-PBF", "kristmann-2022-occ", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-ThesisFF", "kroesl-2020-XREye", "Laschober-2018", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "mayr-2021-wec", "mindek-2017-dsn", "pahr-2024-ieo", "panfili-2019-VAVR", "panfili-2021-myop", "plank-2017-sldg", "polatsek-2018-stv", "Reichinger-2016-spaghetti", "schindler_2020vis", "schuller_reichl-2019-avt", "Semmelrath-2022-IRS", "stappen_SteFAS", "steinkellner-2022-dll", "sundstedt-2013-vag", "thesis-kronsteiner", "trautner-2018-imd", "Vasylevska_Khrystyna-2019-TEFVR", "viola-evr", "Waldin_Nicholas_2016_Colormaps", "Waldin_Nicholas_2016_Individualization", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldner-2013-facetCloudsGI", "waldner-2014-af", "waldner-2017-vph", "waldner-2019-rld" ], "projects": [ "CROSSMOD" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Perception", "__class": "ResearchArea" } ], "keywords": [ "computer graphics", "gauge-figure task", "perceptual visualization", "shape perception" ], "weblinks": [ { "href": "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7478440", "caption": "IEEE Xplore", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 216, "image_height": 205, "name": "bernhard-2016-gft-.jpg", "type": "image/jpeg", "size": 38475, "path": "Publication:bernhard-2016-gft", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/bernhard-2016-gft/bernhard-2016-gft-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/bernhard-2016-gft/bernhard-2016-gft-:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/Perception", "teaser": { "name": "teaser.PNG", "path": "research_area:Perception", "type": "image/png", "size": 259428, "orig_name": "teaser.PNG", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Perception/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend", "vis" ], "publications": [ "antes-2026-maa", "bernhard-2011-maicg", "bernhard-2014-EFD", "bernhard-2014-GTOM", "bernhard-2016-gft", "birkeland_aasmund_2014_pums", "cardoso-2022-rtpercept", "cardoso-thesis", "ERLER-2017-HVR", "eschner-2023-ims", "eschner-blur-2022", "georgiev-2019-cbg", "Groeller_2016_P7", "grossmann-2021-layout", "hecher-2012-MH", "hecher-2014-MH", "HECHER-2017-HDY", "huerbe-2019-BA", "Kathi-2018-VRB", "koch-2019-PR", "Koszticsak-2017-ewt", "kraehsmaier-dsc", "KREUZER-2017-PBF", "kristmann-2022-occ", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-ThesisFF", "kroesl-2020-XREye", "Laschober-2018", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "mayr-2021-wec", "mindek-2017-dsn", "pahr-2024-ieo", "panfili-2019-VAVR", "panfili-2021-myop", "plank-2017-sldg", "polatsek-2018-stv", "Reichinger-2016-spaghetti", "schindler_2020vis", "schuller_reichl-2019-avt", "Semmelrath-2022-IRS", "stappen_SteFAS", "steinkellner-2022-dll", "sundstedt-2013-vag", "thesis-kronsteiner", "trautner-2018-imd", "Vasylevska_Khrystyna-2019-TEFVR", "viola-evr", "Waldin_Nicholas_2016_Colormaps", "Waldin_Nicholas_2016_Individualization", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldner-2013-facetCloudsGI", "waldner-2014-af", "waldner-2017-vph", "waldner-2019-rld" ], "projects": [ "CROSSMOD" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Perception", "__class": "ResearchArea" } ], "keywords": [ "Narrative Visualization", "Flicker", "Visual Attention" ], "weblinks": [ { "href": "http://dx.doi.org/10.1109/TVCG.2014.2346352", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 600, "image_height": 605, "name": "waldner-2014-af-.png", "type": "image/png", "size": 232715, "path": "Publication:waldner-2014-af", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-:thumb{{size}}.png" }, { "description": "Screenshot of the large molecular scene used in the final experiment", "filetitle": "molecularScene", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 7597, "image_height": 1327, "name": "waldner-2014-af-molecularScene.png", "type": "image/png", "size": 3805356, "path": "Publication:waldner-2014-af", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-molecularScene.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-molecularScene:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "waldner-2014-af-paper.pdf", "type": "application/pdf", "size": 6413298, "path": "Publication:waldner-2014-af", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "Preview video", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 1280, "preview_image_height": 720, "name": "waldner-2014-af-Preview video.mp4", "type": "video/mp4", "size": 16497882, "path": "Publication:waldner-2014-af", "preview_name": "waldner-2014-af-Preview video:preview.png", "preview_type": "image/jpeg", "preview_size": 14964, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Preview video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Preview video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Preview video:video.mp4" }, { "description": null, "filetitle": "Submission video", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 1280, "preview_image_height": 720, "name": "waldner-2014-af-Submission video.mp4", "type": "video/mp4", "size": 23904054, "path": "Publication:waldner-2014-af", "preview_name": "waldner-2014-af-Submission video:preview.png", "preview_type": "image/jpeg", "preview_size": 48929, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Submission video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Submission video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-af/waldner-2014-af-Submission video:video.mp4" } ], "projects_workgroups": [ { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "NetVis", "drupal_id": 9472, "name": "Network Visualization", "short_abstract": "This research area aims to investigate how to best visualize graph-typed data, both abstractly as well as in domain-specific applications.", "abstract": "Graph-typed data is ubiquitous, from logistical or transportation networks, through social or correspondence networks, all the way to more abstract biochemical interaction networks. Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "Graph visualization", "Interaction history", "Provenance" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 748, "image_height": 668, "name": "waldner-2014-ghi-.png", "type": "image/png", "size": 118153, "path": "Publication:waldner-2014-ghi", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "waldner-2014-ghi-paper.pdf", "type": "application/pdf", "size": 3116802, "path": "Publication:waldner-2014-ghi", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/waldner-2014-ghi/waldner-2014-ghi-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "Perception", "drupal_id": 4136, "name": "Perception", "short_abstract": "In this area, our research focuses on understanding and using the capabilities of human perception for designing high-quality graphics algorithms and effective visualizations.", "abstract": "In computer graphics we produce images, videos, visualizations and other content that is perceived by humans. By understanding the limits of human perception, we can design and optimize rendering algorithms that perfectly fit users' needs, tailor visualizations to make data better understandable, and effectively draw the user's attention to important information.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/Perception", "teaser": { "name": "teaser.PNG", "path": "research_area:Perception", "type": "image/png", "size": 259428, "orig_name": "teaser.PNG", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/Perception/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "rend", "vis" ], "publications": [ "antes-2026-maa", "bernhard-2011-maicg", "bernhard-2014-EFD", "bernhard-2014-GTOM", "bernhard-2016-gft", "birkeland_aasmund_2014_pums", "cardoso-2022-rtpercept", "cardoso-thesis", "ERLER-2017-HVR", "eschner-2023-ims", "eschner-blur-2022", "georgiev-2019-cbg", "Groeller_2016_P7", "grossmann-2021-layout", "hecher-2012-MH", "hecher-2014-MH", "HECHER-2017-HDY", "huerbe-2019-BA", "Kathi-2018-VRB", "koch-2019-PR", "Koszticsak-2017-ewt", "kraehsmaier-dsc", "KREUZER-2017-PBF", "kristmann-2022-occ", "kroesl-2018-DC", "kroesl-2018-TVS", "kroesl-2019-ICthroughVR", "kroesl-2019-ThesisFF", "kroesl-2020-XREye", "Laschober-2018", "Luidolt-2020-DA", "luidolt-2020-lightperceptionVR", "mayr-2021-wec", "mindek-2017-dsn", "pahr-2024-ieo", "panfili-2019-VAVR", "panfili-2021-myop", "plank-2017-sldg", "polatsek-2018-stv", "Reichinger-2016-spaghetti", "schindler_2020vis", "schuller_reichl-2019-avt", "Semmelrath-2022-IRS", "stappen_SteFAS", "steinkellner-2022-dll", "sundstedt-2013-vag", "thesis-kronsteiner", "trautner-2018-imd", "Vasylevska_Khrystyna-2019-TEFVR", "viola-evr", "Waldin_Nicholas_2016_Colormaps", "Waldin_Nicholas_2016_Individualization", "Waldin_Nicholas_2017_FlickerObserver", "waldin-2017-thesis", "waldner-2013-facetCloudsGI", "waldner-2014-af", "waldner-2017-vph", "waldner-2019-rld" ], "projects": [ "CROSSMOD" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Perception", "__class": "ResearchArea" } ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 362, "image_height": 360, "name": "waldner-2013-facetCloudsGI-image.png", "type": "image/png", "size": 49904, "path": "Publication:waldner-2013-facetCloudsGI", "url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-facetCloudsGI/waldner-2013-facetCloudsGI-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-facetCloudsGI/waldner-2013-facetCloudsGI-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 636, "preview_image_height": 708, "name": "waldner-2013-facetCloudsGI-paper.pdf", "type": "application/pdf", "size": 2184510, "path": "Publication:waldner-2013-facetCloudsGI", "preview_name": "waldner-2013-facetCloudsGI-paper:preview.png", "preview_type": "image/png", "preview_size": 189512, "url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-facetCloudsGI/waldner-2013-facetCloudsGI-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-facetCloudsGI/waldner-2013-facetCloudsGI-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.
\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" } ], "keywords": [ "information management", "ubiquitous displays" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 300, "image_height": 238, "name": "waldner-2013-ubiWM-.png", "type": "image/png", "size": 39630, "path": "Publication:waldner-2013-ubiWM", "url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-ubiWM/waldner-2013-ubiWM-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-ubiWM/waldner-2013-ubiWM-:thumb{{size}}.png" }, { "description": null, "filetitle": "Position Paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "waldner-2013-ubiWM-Position Paper.pdf", "type": "application/pdf", "size": 846295, "path": "Publication:waldner-2013-ubiWM", "url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-ubiWM/waldner-2013-ubiWM-Position Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2013/waldner-2013-ubiWM/waldner-2013-ubiWM-Position Paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "illvisation", "workgroup_id": "vis", "drupal_id": 4171, "drupal_path": "/research/projects/illvisation", "name": "Visual Computing: Illustrative Visualization", "name_de": null, "short_title": "illvisation", "website": null, "status": "active", "short_abstract": "The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness
The central focus of our research is to understand visual abstraction. Understanding means 1. to identify meaningful visual abstractions, 2. to assess their effectiveness for human perception and cognition and 3. to formalize them to be executable on a computational machinery. The outcome of the investigation is useful for designing visualizations for a given scenario or need, whose effectiveness can be quantified and thus the most understandable visualization design can be effortlessly determined. The science of visualization has already gained some understanding of structural visual abstraction. When for example illustrators, artists, and visualization designers convey certain structure, or visually express how things look, we can often provide a scientifically-founded argument whether and why is their expression effective for human cognitive processing. What has not been given sufficient scientific attention to, is advancing the understanding of procedural visual abstraction, in other words investigating visual means that convey what things do or how things work. This missing piece of knowledge would be very useful for visual depiction of processes and dynamics that are omnipresent in science, technology, but also in our everyday lives. The upcoming project will therefore investigate theoretical foundations for visualization of processes. Physiological processes that describe the complex machinery of biological life will be picked as a target scenario. The reason for this choice is two-fold. Firstly, these processes are immensely complex, are carried-out on various spatial and temporal levels simultaneously, and can be sufficiently understood only if all scales are considered. Secondly, physiological processes have been modeled as a result of intensive research in biology, systems biology, and biochemistry and are available in a form of digital data. The goal will be to visually communicate how physiological processes participate on life by considering the limitations of human perceptual and cognitive capabilities. By solving individual visualization problems of this challenging target scenario, the research will provide first pieces of understanding of procedural visual abstractions that are generally applicable, beyond the chosen target domain. Prototype implementation of the developed technology is available at the GitHub repository: https://github.com/illvisation/
\r\n\r\nPrototype implementation of the developed technology is available at the GitHub repository:
\r\nhttps://github.com/illvisation/
cellVIEW is a new tool that provides fast rendering of very large biological macromolecular scenes and is inspired by state-of-the-art computer graphics techniques. Click here for additional information.
\r\n\r\n18.11.2016: Arthur J. Olson, Envisioning the Visible Molecular Cell
\r\n17.10.2016: Kwan-Liu Ma, Emerging Topics for Visualization Research: Part1, Part2
\r\n07.10.2016: Marc Streit, From Visual Exploration to Storytelling and Back Again
\r\n04.12.2015: Jan Palacek, Visual Analysis of Protein Complexes: From Protein Interaction to Cellular Processes
\r\n19.04.2013: Jan Koenderink, Shape in Visual Awareness