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        "id": "wolter-2025-mdv",
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        "title": "Multi-Agent Data Visualization and Narrative Generation",
        "date": "2025-11-03",
        "abstract": "Recent advancements in the field of AI agents have impacted the way we work, enabling greater automation and collaboration between humans and agents. In the data visualization field, multiagent systems can be useful for employing agents throughout the entire data-to-communication pipeline. We present a lightweight multi-agent system that automates the data analysis workflow, from data exploration to generating coherent visual narratives for insight communication. Our approach combines a hybrid multi-agent architecture with deterministic components, strategically externalizing critical logic from LLMs to improve transparency and reliability. The system delivers granular, modular outputs that enable surgical modifications without full regeneration, supporting sustainable human-AI collaboration. We evaluated our system across 4 diverse datasets, demonstrating strong generalizability, narrative quality, and computational efficiency with minimal dependencies.",
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        "event": "1st Workshop on  Logo GenAI, Agents, and the Future of VIS (IEEE VIS 2025)",
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        "location": "Vienna",
        "research_areas": [],
        "keywords": [
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        "tu_id": null,
        "repositum_id": "20.500.12708/225028",
        "title": "Agentic Visualization: Extracting Agent-Based Design Patterns From Visualization Systems",
        "date": "2025-11",
        "abstract": "Autonomous agents powered by large language models are transforming artificial intelligence (AI), creating an imperative for the visualization area. However, our field's focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such agentic visualization that preserve human agency while amplifying analytical capabilities. This article addresses these questions by reinterpreting existing visualization systems with semiautomated or fully automatic AI components through an agentic lens. Based on this analysis, we extract a collection of design patterns for agentic visualization, including agentic roles, communication, and coordination. These patterns provide a foundation for future agentic visualization systems that effectively harness AI agents while maintaining human insight and control.",
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        "journal": "IEEE Computer Graphics and Applications",
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        "publisher": "IEEE COMPUTER SOC",
        "volume": "45",
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        "keywords": [
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            "visualization",
            "design patterns"
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        "repositum_id": "20.500.12708/209410",
        "title": "D-Tour: semi-automatic generation of interactive guided tours for visualization dashboard onboarding",
        "date": "2025-01",
        "abstract": "Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR PROTOTYPE, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.",
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        "doi": "10.1109/TVCG.2024.3456347",
        "issn": "1941-0506",
        "journal": "IEEE Transactions on Visualization and Computer Graphics",
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        "publisher": "IEEE COMPUTER SOC",
        "volume": "31",
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        "keywords": [
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            "onboarding",
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    {
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        "type_id": "phdthesis",
        "tu_id": null,
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        "title": "From Analysis to Communication: Supporting Users in Understanding Complex Spreadsheets and Dashboards",
        "date": "2024-09-26",
        "abstract": "Advancements in big data processing and interactive visualization tools have led to significant changes in how users analyze and explore their data. This thesis aims to\naddress the challenges resulting from these changes through a two-step approach to support users. We first address the issues at the spreadsheet level before moving on to more complex visual representations in a dashboard environment. We use the Fuzzy Spreadsheet approach at the spreadsheet level to include uncertain information in the decision-making process. Our approach augments traditional spreadsheets with uncertain\ninformation where a cell can hold and display a distribution of values, in addition to other contextually relevant information, such as impact and relationship between cells, to convey sensitivity and robustness information to the user. When users transition from spreadsheet representations to advanced visualization tools such as interactive dashboards, they often face challenges related to their use that can lead them to revert to their old, familiar static analysis tools. With the help of dashboard onboarding, authors can communicate the intended use and purpose of their dashboards, along with the\nworkings of visualizations present on the dashboards, to fill the user’s knowledge gap.\nWe created a process model for dashboard onboarding that formalizes and unifies different onboarding strategies for dashboards and facilitates the design and implementation of new\nonboarding approaches. Using this process model as a base and drawing inspiration from the fields of data storytelling and open-world game design, we developed an approach for\ncrafting semi-automated interactive dashboard tours (D-Tours) to produce an onboarding experience tailored to individual users while preserving their agency. We implemented\nthis concept in a tool called D-Tour Prototype which allows authors to create D-Tours from scratch or using automatic templates. Finally, we provide future directions based\non the insights from this thesis to explore the role of AI in the design and development of dashboard onboarding.",
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        "rigorosum": "2024-10-01",
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        "id": "dhanoa-2022-apm",
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        "repositum_id": "20.500.12708/174385",
        "title": "A Process Model for Dashboard Onboarding",
        "date": "2022-06",
        "abstract": "Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non-expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real-world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.",
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        "doi": "10.1111/cgf.14558",
        "issn": "1467-8659",
        "journal": "Computer Graphics Forum",
        "number": "3",
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        "pages": "13",
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        "publisher": "Wiley",
        "volume": "41",
        "research_areas": [],
        "keywords": [
            "CCS Concepts",
            "concepts and paradigms",
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        "title": "Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations ",
        "date": "2021-10-11",
        "abstract": "Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications. A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the inherent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot easily be embedded in a tabular layout. In Fuzzy Spreadsheet, a cell can hold and display a distribution of values. This integrated uncertainty-handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but also with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses. Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells. To evaluate its usability and the perceived mental effort required, we conducted a user study. The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort in almost all tasks tested. ",
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