[
    {
        "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,
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                "firstname": "Maath",
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        ],
        "ac_number": "AC17745765",
        "date_end": "2025",
        "date_start": "2021",
        "doi": "10.34726/hss.2026.137814",
        "open_access": "yes",
        "pages": "185",
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                "firstname": "Jean-Daniel",
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                "searchname": "Jean-Daniel Fekete",
                "is_teacher": false,
                "name": "Jean-Daniel Fekete",
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                "website": "https://www.cg.tuwien.ac.at/staff/RenataRaidou",
                "firstname": "Renata",
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                "searchname": "Renata Raidou",
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                "abstract": "<p>Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex.&nbsp;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.</p>\r\n",
                "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis",
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                    "stoff-2025-pvu",
                    "stoff-concepMap-2021",
                    "stritzel2022",
                    "Strohmayer-2018-BT",
                    "takahashi-2019-acdt",
                    "tekaya-2025-amo",
                    "Thomas-2018",
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                    "vad_viktor-2017-WVE",
                    "vaico",
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        "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.",
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        "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.",
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        "title": "Visual analysis of blow molding machine multivariate time series data",
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        "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.",
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