[
    {
        "id": "musleh-2026-gtu",
        "type_id": "journalpaper_notalk",
        "tu_id": null,
        "repositum_id": "20.500.12708/230849",
        "title": "Guiding through uncertainties in visual analytics: A survey",
        "date": "2026",
        "abstract": "Several uncertainties emerge in each component of the visual analytics (VA) cycle that hinder the user’s ability to make efficient and effective decisions, e.g., through missing data, model approximations, or visual mappings. Well-known VA strategies aim first at making users aware of these uncertainties, often through visual means. When visuals alone are insufficient to accurately quantify or communicate uncertainties, VA designers may rely on guidance to support users’ understanding of these uncertainties throughout the VA cycle. While prior VA research has attempted to conceptualize guidance, the ability and mechanisms for guidance to comprehensively address different sources of uncertainties remain an open question. In this survey, we characterize the relationships between uncertainties and guidance in VA literature. Our key contribution is a taxonomic framework that relates uncertainty sources to relevant guidance strategies and their respective profiles, i.e., roles, scopes, and features. Through this taxonomy, we discuss how guidance addresses uncertainties, identify research gaps, and promote a more comprehensive understanding of guidance strategies to support effective design for uncertainty in VA. Our survey underscores the effectiveness of guidance in navigating uncertainties with context-aware and multi-scope strategies. We highlight challenging opportunities for further research in this space, especially in the adjacent areas of accessibility and onboarding, and suggest new research areas, such as narrative and persuasion guidance to support uncertainty awareness in VA.",
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        "substitute": null,
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        "authors": [
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            5418,
            5628,
            5629,
            5624,
            966,
            1410
        ],
        "articleno": "104739",
        "doi": "10.1016/j.cag.2026.104739",
        "issn": "1873-7684",
        "journal": "Computers & Graphics",
        "pages": "25",
        "publisher": "PERGAMON-ELSEVIER SCIENCE LTD",
        "research_areas": [],
        "keywords": [
            "Guidance",
            "Uncertainty",
            "Visual Analytics",
            "Decision Support Systems"
        ],
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    },
    {
        "id": "perez-messina-2026-fdt",
        "type_id": "inproceedings",
        "tu_id": null,
        "repositum_id": "20.500.12708/230588",
        "title": "From Data to Ficta: A Critical Reflection on Visual Analytics in the Age of Generative Models",
        "date": "2026",
        "abstract": "Over the last decades, the pervasiveness of data and the rate at which they are produced have propelled the rapid growth of Visual Analytics (VA) methods for interactive analysis and decision-making. This foundation is now shifting as computers increasingly not only process data, but also produce them through generative models. Revisiting the notion of synthetic data, we oppose data to capta (observational inscriptions) and introduce ficta: model-produced outputs that enter VA sessions as an analytic substrate with their own means of interactive production. In ficta-driven workflows, the substrate of analysis is no longer a static representation from which knowledge can be distilled; interaction becomes entangled with what is available to observe, and visualizations come to mediate not a single world but a plurality of model-admissible alternatives. We argue that this breaks VA’s classic data-driven epistemic contract and motivates a reevaluation of core assumptions behind knowledge production in the age of generativeness.",
        "authors_et_al": false,
        "substitute": null,
        "main_image": null,
        "sync_repositum_override": "lecturer",
        "repositum_presentation_id": null,
        "authors": [
            5624,
            966,
            5625
        ],
        "booktitle": "EuroVA 2026 : EuroVis Workshop on Visual Analytics",
        "date_from": "2026-06-08",
        "date_to": "2026-06-12",
        "doi": "10.2312/eurova.20261008",
        "editor": "Villanova, Anna and Archambault, Daniel and Laramee, Robert S. and Xu, Kai and Fellner, Dieter",
        "event": "17th International EuroVis Workshop on Visual Analytics (EuroVA 2026) co-located with the Eurographics Conference on Visualization (EuroVis 2026)",
        "isbn": "978-3-03868-309-4",
        "lecturer": [
            5624
        ],
        "location": "Nottingham",
        "open_access": "yes",
        "pages": "7",
        "publisher": "The Eurographics Association",
        "research_areas": [],
        "keywords": [
            "Visualization theory",
            "Synthetic Data",
            "Visual Analytics"
        ],
        "weblinks": [],
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        "url": "https://www.cg.tuwien.ac.at/research/publications/2026/perez-messina-2026-fdt/",
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]