[ { "id": "Musleh_PhD", "type_id": "phdthesis", "tu_id": null, "repositum_id": "20.500.12708/224040", "title": "Guided Visual Analytics for Decision Making under Uncertainty", "date": "2026-03", "abstract": "Visual Analytics (VA) has emerged from the need to optimize decision making by involving human reasoning in sense making. The development of VA has been facilitated by significant technological advances in modern computer graphics and data processing capabilities. Involving humans in the loop aims to address high-risk scenarios where artificial intelligence (AI) automated approaches are insufficient. One active area of research with VA is the development of methods that enable the user to make efficient and effective decisions under high uncertainty. Yet, the field of VA research has not fully understood how user attitude, namely trust and confidence, interplay in VA decision making under uncertainty. Properties of the user attitude play a crucial role in optimizing VA decision making, but they are challenging to externalize and evaluate. For instance, user confidence in their decision emerges as an important indicator of effectiveness when the correctness of the decision cannot be measured. In this dissertation, we explore the use of guidance techniques to address uncertainties in VA decision making, focusing on scenarios where the correctness of decisions cannot be definitively established. Throughout this work, we learned that a multidimensional guidance mechanism can address uncertainties more effectively when uncertainties are challenging to quantify and visualize, especially in the case of subjective uncertainty. However, evaluating the effectiveness of guidance approaches requires a more comprehensive analysis of the interplay between trust and confidence within the sense-making process. Using provenance networks and SNA metrics can provide a more reliable and comprehensive assessment of user confidence, indicating that such approaches can be employed to support co-adaptive guidance.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "title,abstract,date,keywords", "repositum_presentation_id": null, "authors": [ 1867 ], "ac_number": "AC17745765", "date_end": "2025", "date_start": "2021", "doi": "10.34726/hss.2026.137814", "open_access": "yes", "pages": "185", "reviewer_1": [ 5252 ], "reviewer_2": [ 5243 ], "rigorosum": "2025-11", "supervisor": [ 1410 ], "research_areas": [ "InfoVis", "MedVis" ], "keywords": [ "Visualization", "Visual Analytics", "Guidance", "Trust", "Confidence" ], "weblinks": [], "files": [ { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Musleh_PhD-thesis.pdf", "type": "application/pdf", "size": 6169556, "path": "Publication:Musleh_PhD", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/Musleh_PhD/Musleh_PhD-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/Musleh_PhD/Musleh_PhD-thesis:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/Musleh_PhD/", "__class": "Publication" }, { "id": "musleh-2025-trustme", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/215604", "title": "TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance", "date": "2025-04-21", "abstract": "Guidance-enhanced approaches are used to support users in making sense of their data and overcoming challenging analytical scenarios. While recent literature underscores the value of guidance, a lack of clear explanations to motivate system interventions may still negatively impact guidance effectiveness. Hence, guidance-enhanced VA approaches require meticulous design, demanding contextual adjustments for developing appropriate explanations. Our paper discusses the concept of explainable guidance and how it impacts the user-system relationship-specifically, a user's trust in guidance within the VA process. We subsequently propose a model that supports the design of explainability strategies for guidance in VA. The model builds upon flourishing literature in explainable AI, available guidelines for developing effective guidance in VA systems, and accrued knowledge on user-system trust dynamics. Our model responds to challenges concerning guidance adoption and context-effectiveness by fostering trust through appropriately designed explanations. To demonstrate the model's value, we employ it in designing explanations within two existing VA scenarios. We also describe a design walk-through with a guidance expert to showcase how our model supports designers in clarifying the rationale behind system interventions and designing explainable guidance.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 703, "image_height": 706, "name": "musleh-2025-trustme-.png", "type": "image/png", "size": 75894, "path": "Publication:musleh-2025-trustme", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-:thumb{{size}}.png" }, "sync_repositum_override": "event,number,pages_from,pages_to,volume", "repositum_presentation_id": null, "authors": [ 1867, 1410, 5418 ], "doi": "10.1109/TVCG.2025.3562929", "event": "IEEE Vis 2025", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "number": "10", "open_access": "yes", "pages": "17", "pages_from": "8040", "pages_to": "8056", "publisher": "IEEE COMPUTER SOC", "volume": "31", "research_areas": [ "InfoVis" ], "keywords": [ "Explainability", "Explainable Guidance", "User Trust", "Visual Analytics" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 703, "image_height": 706, "name": "musleh-2025-trustme-.png", "type": "image/png", "size": 75894, "path": "Publication:musleh-2025-trustme", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/musleh-2025-trustme-:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2025-trustme/", "__class": "Publication" }, { "id": "musleh-2024-conan", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/205807", "title": "ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty", "date": "2025-02", "abstract": "User confidence plays an important role in guided visual data analysis scenarios, especially when uncertainty is involved in the analytical process. However, measuring confidence in practical scenarios remains an open challenge, as previous work relies primarily on self-reporting methods. In this work, we propose a quantitative approach to measure user confidence—as opposed to trust—in an analytical scenario. We do so by exploiting the respective user interaction provenance graph and examining the impact of guidance using a set of network metrics. We assess the usefulness of our proposed metrics through a user study that correlates results obtained from self-reported confidence assessments and our metrics—both with and without guidance. The results suggest that our metrics improve the evaluation of user confidence compared to available approaches. In particular, we found a correlation between self-reported confidence and some of the proposed provenance network metrics. The quantitative results, though, do not show a statistically significant impact of the guidance on user confidence. An additional descriptive analysis suggests that guidance could impact users' confidence and that the qualitative analysis of the provenance network topology can provide a comprehensive view of changes in user confidence. Our results indicate that our proposed metrics and the provenance network graph representation support the evaluation of user confidence and, subsequently, the effective development of guidance in VA.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 8170, "image_height": 5961, "name": "musleh-2024-conan-.png", "type": "image/png", "size": 9874701, "path": "Publication:musleh-2024-conan", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,articleno,event,number,volume", "repositum_presentation_id": null, "authors": [ 1867, 5418, 1850, 1410 ], "articleno": "e15272", "doi": "10.1111/cgf.15272", "event": "EuroVis 2025", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "1", "open_access": "yes", "pages": "18", "publisher": "WILEY", "volume": "44", "research_areas": [], "keywords": [ "decision making", "uncertainty", "user confidence", "visual analytics", "guided visual data analysis" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 8170, "image_height": 5961, "name": "musleh-2024-conan-.png", "type": "image/png", "size": 9874701, "path": "Publication:musleh-2024-conan", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/musleh-2024-conan-:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/musleh-2024-conan/", "__class": "Publication" }, { "id": "musleh-2023-ugi", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/158183", "title": "Uncertainty guidance in proton therapy planning visualization", "date": "2023-02-20", "abstract": "We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Graphical Abstract", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 1039, "name": "musleh-2023-ugi-Graphical Abstract.jpg", "type": "image/jpeg", "size": 235392, "path": "Publication:musleh-2023-ugi", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract:thumb{{size}}.png" }, "sync_repositum_override": "event", "repositum_presentation_id": null, "authors": [ 1867, 1445, 1863, 5236, 166, 1410 ], "doi": "10.1016/j.cag.2023.02.002", "event": "VCBM 2023", "issn": "1873-7684", "journal": "Computers and Graphics", "open_access": "yes", "pages_from": "166", "pages_to": "179", "publisher": "PERGAMON-ELSEVIER SCIENCE LTD", "volume": "111", "research_areas": [ "MedVis" ], "keywords": [ "Visual analytics", "Applied computing", "Decision support systems" ], "weblinks": [], "files": [ { "description": null, "filetitle": "Graphical Abstract", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1333, "image_height": 1039, "name": "musleh-2023-ugi-Graphical Abstract.jpg", "type": "image/jpeg", "size": 235392, "path": "Publication:musleh-2023-ugi", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/musleh-2023-ugi-Graphical Abstract:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/musleh-2023-ugi/", "__class": "Publication" }, { "id": "musleh-2022-mam5", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/142512", "title": "Visual analysis of blow molding machine multivariate time series data", "date": "2022-07-11", "abstract": "The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Graphical Abstract", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 685, "image_height": 462, "name": "musleh-2022-mam5-Graphical Abstract.webp", "type": "image/webp", "size": 41652, "path": "Publication:musleh-2022-mam5", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-Graphical Abstract.webp", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-Graphical Abstract:thumb{{size}}.png" }, "sync_repositum_override": "abstract,date", "repositum_presentation_id": null, "authors": [ 1867, 5213, 5214 ], "doi": "10.1007/s12650-022-00857-4", "issn": "1875-8975", "journal": "Journal of Visualization", "open_access": "yes", "pages": "14", "pages_from": "1329", "pages_to": "1342", "publisher": "Springer", "volume": "25", "research_areas": [ "InfoVis" ], "keywords": [ "Time series data", "Unsupervised machine learning", "Visualization" ], "weblinks": [ { "href": "https://link.springer.com/article/10.1007/s12650-022-00857-4", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "Graphical Abstract", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 685, "image_height": 462, "name": "musleh-2022-mam5-Graphical Abstract.webp", "type": "image/webp", "size": 41652, "path": "Publication:musleh-2022-mam5", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-Graphical Abstract.webp", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-Graphical Abstract:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "musleh-2022-mam5-paper.pdf", "type": "application/pdf", "size": 1034180, "path": "Publication:musleh-2022-mam5", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/musleh-2022-mam5-paper:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/musleh-2022-mam5/", "__class": "Publication" }, { "id": "musleh_maath_2021_mam", "type_id": "poster", "tu_id": 300507, "repositum_id": "20.500.12708/58636", "title": "Agritology: A Decision Support System for Local Farmers in Malta and Palestine", "date": "2021-09", "abstract": " In this project, we are utilizing the potential of Semantic Web in organizing shareable knowledge. We constructed an ontology of the farming process that is reusable and interoperable in the domain of Agriculture. The ontology supports the decision making of farmers in Malta and Palestine. The web application uses the ontology to share knowledge based primarily on user input and other external data sources. In addition to English, information is also presented in Maltese and Arabic in aim to preserve domain-specific vocabulary in these languages.", "authors_et_al": false, "substitute": null, "main_image": { "description": "GUI of the Solution", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 847, "image_height": 784, "name": "musleh_maath_2021_mam-image.png", "type": "image/png", "size": 188463, "path": "Publication:musleh_maath_2021_mam", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/musleh_maath_2021_mam-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/musleh_maath_2021_mam-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ 5215, 1867 ], "cfp": { "name": "3rd International Workshop on Semantics for Biodiversity.pdf", "type": "application/pdf", "error": "0", "size": "141009", "orig_name": "3rd International Workshop on Semantics for Biodiversity.pdf", "ext": "pdf" }, "date_from": "2021-09-11", "date_to": "2021-09-18", "event": "3rd International Workshop on Semantics for Biodiversity (S4BioDiv)", "journal": "CEUR Workshop Proceedings ", "location": "Bolzano, Italy", "open_access": "yes", "pages": "5", "pages_from": "1", "pages_to": "5", "research_areas": [ "InfoVis" ], "keywords": [ "Semantic Web", "Urban Agriculture", "Ontology" ], "weblinks": [ { "href": "http://ceur-ws.org/Vol-2969/paper3-s4biodiv.pdf", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": "GUI of the Solution", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 847, "image_height": 784, "name": "musleh_maath_2021_mam-image.png", "type": "image/png", "size": 188463, "path": "Publication:musleh_maath_2021_mam", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/musleh_maath_2021_mam-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/musleh_maath_2021_mam-image:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath_2021_mam/", "__class": "Publication" }, { "id": "musleh_maath-2021-mam1", "type_id": "inproceedings", "tu_id": 300419, "repositum_id": "20.500.12708/55641", "title": "Visual Analysis of Industrial Multivariate Time Series", "date": "2021-09", "abstract": "The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field.", "authors_et_al": false, "substitute": null, "main_image": { "description": "GUI of the Solution", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 916, "image_height": 621, "name": "musleh_maath-2021-mam1-image.png", "type": "image/png", "size": 369431, "path": "Publication:musleh_maath-2021-mam1", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/musleh_maath-2021-mam1-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/musleh_maath-2021-mam1-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1867, 5213, 5214 ], "address": "New York, NY, USA", "booktitle": "The 14th International Symposium on Visual Information Communication and Interaction", "cfp": { "name": "Submissions - VINCI 2021.pdf", "type": "application/pdf", "error": "0", "size": "2566211", "orig_name": "Submissions - VINCI 2021.pdf", "ext": "pdf" }, "date_from": "2021-09-06", "date_to": "2021-09-08", "doi": "10.1145/3481549.3481557", "editor": "Karsten Klein, Michael Burch, Daniel Limberger, Matthias Trapp", "event": "VINCI 2021: The 14th International Symposium on Visual Information Communication and Interaction", "first_published": "2021-09", "isbn": "9781450386470", "lecturer": [ 1867 ], "location": "Potsdam, germany", "note": "Best Short Paper Award", "open_access": "no", "pages": "5", "pages_from": "1", "pages_to": "5", "publisher": "Association for Computing Machinery", "series": "VINCI 2021", "research_areas": [ "InfoVis" ], "keywords": [ "time series data", "unsupervised machine learning", "visualization" ], "weblinks": [ { "href": "https://dl.acm.org/doi/10.1145/3481549.3481557", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": "GUI of the Solution", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 916, "image_height": 621, "name": "musleh_maath-2021-mam1-image.png", "type": "image/png", "size": 369431, "path": "Publication:musleh_maath-2021-mam1", "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/musleh_maath-2021-mam1-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/musleh_maath-2021-mam1-image:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/", "__class": "Publication" }, { "id": "musleh_maath-2021-mam2", "type_id": "masterthesis", "tu_id": null, "repositum_id": null, "title": "Visual Analysis of Industrial Multivariate Time-Series Data: Effective Solution to Maximise Insights from Blow Moulding Machine Sensory Data", "date": "2021-06-21", "abstract": "Developments in the field of data analytics provides a boost for small-sized factories. These factories are eager to take full advantage of the potential insights in the remotely collected data to minimise cost and maximise quality and profit. This project aims to process, cluster and visualise sensory data of a blow moulding machine in a plastic production factory. In collaboration with Lean Automation, we aim to develop a data visualisation solution to enable decision-makers in a plastic factory to improve their production process. We will investigate three different aspects of the solution: methods for processing multivariate time-series data, clustering approaches for the sensory-data cultivated, and visualisation techniques that maximises production process insights. We use a formative evaluation method to develop a solution that meets partners' requirements and best practices within the field. Through building the MTSI dashboard tool, we hope to answer questions on optimal techniques to represent, cluster and visualise multivariate time series data. 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