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"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.
Links
",
"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.
Links
",
"start_date": "2023-12-01",
"end_date": "2024-11-30",
"leader_id": 1110,
"logo": {
"name": "logo.png",
"path": "project:d9555",
"type": "image/png",
"size": 8228091,
"orig_name": "logo.png",
"thumb_url": "https://www.cg.tuwien.ac.at/research/projects/d9555/logo:thumb{{size}}.png",
"thumb_image_sizes": [
120,
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]
},
"funding_organisations": [],
"research_areas": [
"InfoVis",
"Rendering"
],
"publications": [
"komon-2025-dco",
"komon-2025-webigeo",
"komon-kimmersdorfer-2025-weBIGeo",
"muth-2026-clouds"
],
"url": "https://www.cg.tuwien.ac.at/research/projects/weBIGeo",
"__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"
],
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"2015-image-coll",
"2017-kouril-illcell",
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"AA2020",
"aigner-2025-vhv",
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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\".
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Hypotheses
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.
Approach
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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Primary researchers involved
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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