44-Issue 3

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https://diglib.eg.org/handle/10.2312/3607144

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Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Borrelli, Gabriel; Ittermann, Till; Linsen, Lars; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Through training and gathered experience, domain experts attain a mental model of the uncertainties inherent in the visual analytics processes for their respective domain. For an accurate data analysis and trustworthiness of the analysis results, it is essential to include this knowledge and consider this model of uncertainty during the analytical process. For multi-dimensional data analysis, Parallel Coordinates are a widely used approach due to their linear scalability with the number of dimensions and bijective (i.e., loss-less) data transformation. However, selections in Parallel Coordinates are typically achieved by a binary brushing operation on the axes, which does not allow the users to map their mental model of uncertainties to their selection. We, therefore, propose Probabilistic Parallel Coordinates as a natural extension of the classical Parallel Coordinates approach that integrates probabilistic brushing on the axes. It supports the interactive modeling of a probability distribution for each parallel coordinate. The selections on multiple axes are combined accordingly. An efficient rendering on a compute shader facilitates interactive frame rates. We evaluated our open-source tool with practitioners and compared it to classical Parallel Coordinates on multiple regression and uncertain selection tasks in user studies.
Coupling Guidance and Progressiveness in Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Pérez-Messina, Ignacio; Angelini, Marco; Ceneda, Davide; Tominski, Christian; Miksch, Silvia; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data size and complexity in Visual Analytics (VA) pose significant challenges for VA systems and VA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies of G4P and P4G.
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Rauscher, Julius; Dennig, Frederik L.; Schlegel, Udo; Keim, Daniel A.; Fuchs, Johannes; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
One-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures.We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Chen, Juntong; Wu, Jiang; Guo, Jiajing; Mohanty, Vikram; Li, Xueming; Ono, Jorge Piazentin; He, Wenbin; Ren, Liu; Liu, Dongyu; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation.We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Yan, Xinyuan; Xuan, Xiwei; Ono, Jorge Piazentin; Guo, Jiajing; Mohanty, Vikram; Kumar, Shekar Arvind; Gou, Liang; Wang, Bei; Ren, Liu; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor-intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human-in-the-loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state-of-the-art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models.
Either Or: Interactive Articles or Videos for Climate Science Communication
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Poehls, Jeran; Meuschke, Monique; Carvalhais, Nuno; Lawonn, Kai; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Effective communication of climate science is critical as climate-related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision-making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text-image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency. Sample analysis (n = 622) displayed equivalent retention by viewers between both formats. Metrics assessing interactivity found no correlation between interactivity and information retention. However, a stark contrast was observed in the time viewers spent engaging with each format. The video format was 29% more efficient with information taught over a period of time vs. the article. Additionally, retention on the video format worsened with age (P = 0.004) while retention on the article format improved with education (P = 0.038). These results align with previous findings in literature.
FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Shrestha, Hilson; Cachel, Kathleen; ALKHATHLAN, MALLAK; Rundensteiner, Elke; Harrison, Lane; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real-world scenario- where diverse opinions are represented by a larger collection of rankings- remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks.We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision-making in ranking based decision-making contexts. Code, datasets and demo video available at: osf.io/d7cwk
Accessible Text Descriptions for UpSet Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) McNutt, Andrew; McCracken, Maggie K.; Eliza, Ishrat Jahan; Hajas, Daniel; Wagoner, Jake; Lanza, Nate; Wilburn, Jack; Creem-Regehr, Sarah; Lex, Alexander; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
Necessary but not Sufficient: Limitations of Projection Quality Metrics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Machado, Alister; Behrisch, Michael; Telea, Alexandru; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
High-dimensional data analysis often uses dimensionality reduction (DR, also called projection) to map data patterns to human-digestible visual patterns in a 2D scatterplot. Yet, DR methods may fail to show true data patterns and/or create visual patterns that do not represent any data patterns. Projection Quality Metrics (PQMs) are used as objective measures to gauge the above process: the higher a projection's scores in PQMs, the more it is deemed faithful to the data it represents. We show that, while PQMs can be used as exclusion criteria - low values usually mean poor projections - the converse does not always hold. For this, we develop a technique to automatically generate projections that score similar or even higher PQM values than projections created by well-known techniques, but show different, often confusing, visual patterns. Our results show that accepted PQMs cannot be used as an exclusive way to tell whether a projection yields accurate and interpretable visual patterns - in this sense, PQMs play a role akin to that of summary statistics in exploratory data analysis. We also show that not all studied metrics can be fooled equally well, suggesting a ranking of metrics in their ability to reliably capture quality.
Beyond Entertainment: An Investigation of Externalization Design in Video Games
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Becker, Franziska; Warnking, Rene Pascal; Brückler, Hendrik; Blascheck, Tanja; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data-much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization.
Enhancing Material Boundary Visualizations in 2D Unsteady Flow through Local Reference Frame Transformations
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Zhang, Xingdi; Rautek, Peter; Theußl, Thomas; Hadwiger, Markus; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We present a novel technique for the extraction, visualization, and analysis of material boundaries and Lagrangian coherent structures (LCS) in 2D unsteady flow fields relative to local reference frame transformations. In addition to the input flow field, we leverage existing methods for computing reference frames adapted to local fluid features, in particular those that minimize the observed time derivative. Although, by definition, transforming objective tensor fields between reference frames does not change the tensor field, we show that transforming objective tensors, such as the finite-time Lyapunov exponent (FTLE) or Lagrangian-averaged vorticity deviation (LAVD), or the second-order rate-of-strain tensor, into local reference frames that are naturally adapted to coherent fluid structures has several advantages: (1) The transformed fields enable analyzing LCS in space-time visualizations that are adapted to each structure; (2) They facilitate extracting geometric features, such as iso-surfaces and ridge lines, in a straightforward manner with high accuracy. The resulting visualizations are characterized by lower geometric complexity and enhanced topological fidelity. To demonstrate the effectiveness of our technique, we measure geometric complexity and compare it with iso-surfaces extracted in the conventional reference frame. We show that the decreased geometric complexity of the iso-surfaces in the local reference frame, not only leads to improved geometric and topological results, but also to a decrease in computation time.
DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hoque, Naimul; Sultanum, Nicole; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Dashboard guidance helps dashboard users better navigate interactive features, understand the underlying data, and assess insights they can potentially extract from dashboards. However, authoring dashboard guidance is a time consuming task, and embedding guidance into dashboards for effective delivery is difficult to realize. In this work, we contribute DASHGUIDE, a framework and system to support the creation of interactive dashboard guidance with minimal authoring input. Given a dashboard and a communication goal, DASHGUIDE captures a sequence of author-performed interactions to generate guidance materials delivered as playable step-by-step overlays, a.k.a., dashboard tours. Authors can further edit and refine individual tour steps while receiving generative assistance. We also contribute findings from a formative assessment with 9 dashboard creators, which helped inform the design of DASHGUIDE; and findings from an evaluation of DASHGUIDE with 12 dashboard creators, suggesting it provides an improved authoring experience that balances efficiency, expressiveness, and creative freedom.
Sca2Gri: Scalable Gridified Scatterplots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Frey, Steffen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Scatterplots are widely used in exploratory data analysis. Representing data points as glyphs is often crucial for in-depth investigation, but this can lead to significant overlap and visual clutter. Recent post-processing techniques address this issue, but their computational and/or visual scalability is generally limited to thousands of points and unable to effectively deal with large datasets in the order of millions. This paper introduces Sca2Gri (Scalable Gridified Scatterplots), a grid-based post-processing method designed for analysis scenarios where the number of data points substantially exceeds the number of glyphs that can be reasonably displayed. Sca2Gri enables interactive grid generation for large datasets, offering flexible user control of glyph size, maximum displacement for point to cell mapping, and scatterplot focus area. While Sca2Gri's computational complexity scales cubically with the number of cells (which is practically bound to thousands for legible glyph sizes), its complexity is linear with respect to the number of data points, making it highly scalable beyond millions of points.
Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Boucher, Magdalena; AlKadi, Mashael; Bach, Benjamin; Aigner, Wolfgang; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed-method eye-tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Paulovich, Fernando V.; Arleo, Alessio; Elzen, Stef van den; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Nylund, Kai; Mankoff, Jennifer; Potluri, Venkatesh; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib fgures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib fgures. We fnd that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib fgure.
Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Miller, Jacob; Bhatia, Dhruv; Purchase, Helen; Kobourov, Stephen; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non-Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non-Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations.
Player-Centric Shot Maps in Table Tennis
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Erades, Aymeric; Vuillemot, Romain; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Shot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data from broadcast videos. Our findings show that the technique enables capturing insights that were not visible with the absolute coordinate system, particularly in understanding regions that are reachable and those close to the pivot area of the player.
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Huang, Haiwen; Chen, Juntong; Wang, Changbo; Li, Chenhui; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
As urbanization accelerates, urban performance has become a growing concern, impacting every aspect of residents' lives. However, urban performance exploration is a tedious and highly subjective process for users. Users need to manually collect and integrate various information, or spend a large amount of time and effort due to the steep learning curves of existing specialized tools. To address these challenges, we introduce SUPQA, a novel approach for urban performance exploration using natural language as input and interactive geographic visualizations as output. Our approach leverages Large Language Models (LLMs) to effectively interpret user intents and quantify various urban performance measures. We integrate progressive navigation and multi-geographic scale analysis in our visualization system, explaining the reasoning process and streamlining users' decision-making workflow. Two usage scenarios and evaluations demonstrate the effectiveness of SUPQA in helping residents and planners acquire desired information more efficiently and enhancing the quality of decision-making.
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Zhe; Moreland, Kenneth; Larsen, Matthew; Kress, James; Childs, Hank; Li, Guan; Shan, Guihua; Pugmire, David; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.

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