EuroVisPosters2025

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607191

Browse

Now showing 1 - 20 of 26

Characterizing the Performance of Counterfactual and Correlation Guidance via Dataset Perturbations
(The Eurographics Association, 2025) Wang, Arran Zeyu; Borland, David; Gotz, David; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Guidance methods are often employed in visual analytics systems to help users navigate complex datasets and discover meaningful insights. Guidance based on correlation is a common method that can steer users towards closely related variables. However, recent work has shown that guidance based on counterfactual subsets can more effectively capture and surface causal relationships. In this work we further explore these guidance methods by characterizing their performance by systematically introducing perturbations in both the data points generated from a ground truth causal graph, and the causal relationships in the graph itself. Our results indicate that while both guidance types exhibit similar sensitivity to global data point perturbations, counterfactual guidance can better capture perturbations affecting only a single dimension, and more effectively reflect changes in causal link strengths, indicating an improved ability to capture narrow data changes and causal relationships.
TractMMR: Tractography Streamline Rating through User-guided Matchmaking
(The Eurographics Association, 2025) Vink, Ruben; Vilanova, Anna; Chamberland, Maxime; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Diffusion MRI tractography suffers from an ever-increasing parameter problem in which little to no semantic connection exists between input parameters and output tractogram. We present an approach for users to semantically interact with their data in order to produce a ranking over the parameter space which can be used to filter outputs and in downstream tasks to improve parameter selection. Our approach is a first step in bringing visual analytics closer to daily neuropractice by providing users a direct semantic interaction with their data.
Data-driven Education on Biodiversity through Visualizing Spatial Co-occurrence Clusters
(The Eurographics Association, 2025) Arguedas, Raquel; Poddar, Madhav; Sancho-Chavarria, Lilliana; Beck, Fabian; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Educational efforts have raised awareness of biodiversity and ecosystems, but tools that enable broad audiences to explore and understand them through data collected by researchers and citizens remain limited. We propose a clustering approach that visually reveals co-occurrences of species from specific sightings. The interface offers two views: one showing co-occurrence clusters in projected space, the other highlighting them on a map.
Certainly Uncertain: Reintroducing Uncertainty in Visualizations
(The Eurographics Association, 2025) Rajendran, Sandhya; Arleo, Alessio; Miksch, Silvia; Tuscher, Michaela; Filipov, Velitchko Andreev; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Information Diffusion (ID) is shaped by uncertainty, yet most visualizations overlook it, leading to oversimplified or misleading interpretations. This work enhances two existing ID visualizations by integrating uncertainty through visual encodings within the original research goals. We are exploring how visualizing uncertainty might influence interpretation, including the potential for signal suppression or amplification. We discuss design alternatives and insights that apply to visualizing uncertainty in two existing visualization techniques. Future work directions are focusing on evaluating the designs and eliciting user feedback and comments on the interpretability and intuitiveness of the proposed uncertainty visualization encodings.
Towards a Software Framework for Evaluating the Visualization Literacy of Large Language Models
(The Eurographics Association, 2025) Jobst, Adrian; Atzberger, Daniel; Scheibel, Willy; Döllner, Jürgen; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Large Language Models (LLMs) are increasingly integrated into Natural Language Interfaces (NLIs) for visualizations, enabling users to inquire about visualizations through natural language. This work introduces a software framework for evaluating LLMs' visualization literacy, i.e., their ability to interpret and answer questions about visualizations. Our framework generates a set of data points across different LLMs, prompts, and question types, allowing for in-depth analysis. We demonstrate its utility by two experiments, examining the impact of the temperature parameter and predefined answer choices.
EuroVis 2025 Posters: Frontmatter
(The Eurographics Association, 2025) Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Kickin' Scarves: Time-Oriented Visual Comparison of Soccer Trajectories
(The Eurographics Association, 2025) Mertz, Tobias; Kohlhammer, Jörn; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Line-based trajectory drawings face several shortcomings in time-oriented visual comparison scenarios, primarily in their facilitation of the comparison itself as well as in their representation of time. To investigate these challenges, we perform an exploratory study focused on the application of scarf plots to the evaluation of tactics performance in soccer games. While our visual analytics prototype, Kickin' Scarves, manages to avoid the shortcomings of line-based trajectory visualizations, the application of scarf plots to real-world problems poses several design challenges that have not yet been addressed in research.
Towards Scalable Out-of-Core Volume Rendering for High-Performance Visualization of 3D Temporal Multivariate Gas and Fluid Data
(The Eurographics Association, 2025) Thebault, Antoine; Prévost, Stéphanie; Lucas, Laurent; Brenner, Leonardo; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Scientific visualization enables a deeper insight into the data structure and properties. For Gas and fluid simulations, particularly those resulting from real-world gas captures or Computational Fluid Dynamics (CFD) models, this requires the analysis and integration of multivariate attributes alongside dynamic temporal variations. We present an ongoing work to develop a scalable, Out-of-core volume rendering application designed for massive 3D temporal multivariate datasets. Our rendering framework, called FRIGAS (Fluid Rendering Infrastructure for Gas and Atmospheric Simulations), introduces a novel pipeline which leverages improvements in Direct Volume Rendering (DVR) techniques for large scale visualizations, addressing constraints regarding temporal structured data and multivariate analysis for interactive spatial and temporal navigation without HPC resources.
ReVISitPy: Python Bindings for the reVISit Study Framework
(The Eurographics Association, 2025) Shrestha, Hilson; Wilburn, Jack; Bollen, Brian; McNutt, Andrew M.; Lex, Alexander; Harrison, Lane; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
User experiments are an important part of visualization research, yet they remain costly, time-consuming to create, and difficult to prototype and pilot. The process of prototyping a study-from initial design to data collection and analysis-often requires the use of multiple systems (e.g. webservers and databases), adding complexity. We present reVISitPy, a Python library that enables visualization researchers to design, pilot deployments, and analyze pilot data entirely within a Jupyter notebook. Re- VISitPy provides a higher-level Python interface for the reVISit Domain-Specific Language (DSL) and study framework, which traditionally relies on manually authoring complex JSON configuration files. As study configurations grow larger, editing raw JSON becomes increasingly tedious and error-prone. By streamlining the configuration, testing, and preliminary analysis workflows, reVISitPy reduces the overhead of study prototyping and helps researchers quickly iterate on study designs before full deployment through the reVISit framework.
MECpace: A Visual Analytics Tool for Comparing Multiple Embedding Spaces
(The Eurographics Association, 2025) Joshi, Rachit; Zinjarde, Purva; Humayoun, Shah Rukh; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Embeddings play a crucial role in machine learning (ML) by representing high-dimensional data in a lower-dimensional space, enhancing model efficiency and interoperability. Fine-grained analysis of embeddings enables optimization of model architectures, refinement of datasets, and more effective parameter adjustments. We introduce MECpace (Multiple Embedded Comparison Spaces), a web-based visualization tool designed to facilitate the comparison of multiple embedding spaces through intuitive visualizations. MECpace supports rapid comparisons of object neighbors across models using parallel coordinate plots and scatter plots, as well as pairwise comparisons through an integrated matrix-scatter view that combines scatter plots and histograms. Interactive features such as filtering and zooming enable seamless exploration of large datasets. By providing a comprehensive view of embedding similarities across multiple models, MECpace enhances decision-making in the ML pipeline.
Incorporating 3D-Rendered Materials in Visualization
(The Eurographics Association, 2025) Piliouras, Sotiris; Dragicevic, Pierre; Beaudouin-Lafon, Michel; Tsandilas, Theophanis; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
We investigate how 3D-rendered materials can support expressive forms of information visualization. We introduce an early snapshot of our design space, describing how inherent material properties and their state or structural transformations can be used as visual channels or simply as contextual attributes for sensory activation. We explore the potential of rendered materials to evoke emotional engagement, curiosity, aesthetic pleasure, and crossmodal sensory experiences.
Automated Refined Comic Generation: From Investigation Provenance to Data Comics using Visual Narrative Structure
(The Eurographics Association, 2025) Roggenbuck, Kay Arne; Vilanova, Anna; Elzen, Stef van den; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Visual analytics has become an important approach for criminal investigations due to the increasing amount of physical and digital data related to cases. Although state-of-the-art tools are used daily to search for evidence in the data and report the investigator's findings, building such reports remains a labor-intensive manual process. Furthermore, these reports commonly contain only a manually selected set of the investigation results, but not how these results were derived. This lack of information about the chain of evidence not only weakens reproducibility and transparency, but also makes the evidence vulnerable by jurists in court. Instead of textual reports we believe annotated visuals of the actual data exploration process better portray what the investigators did and how they came to the evidence. To this end, we introduce ARC, a framework for automatically generating comic summaries for digital investigations based on the Visual Narrative Structure from comic theory. Especially, ARC is the first framework that fully automatically generates and refines comic summaries based on interactions with investigation tools.
A Method for Optimizing the Rendering Order of Scatterplots
(The Eurographics Association, 2025) Liu, Liqun; Ruddle, Roy A.; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Rendering order is crucial for generating effective scatterplots, as a rendering sequence can cause anomalous data points to be obscured by others. This issue is particularly significant in the field of explainable artificial intelligence (XAI), where large volumes of data can prevent users from observing misclassified instances. This poster introduces a novel method for sorting data points and rendering them sequentially to reduce the likelihood of anomalous points being hidden. First, we normalize the two coordinates of the scatterplots to mitigate the impact of differing value ranges. Next, we propose a method for calculating the anomaly index of each data point. Finally, we sort the data points based on their anomaly index and render them sequentially.We compare our method with existing approaches on scatterplots generated by dimensionality reduction (DR) techniques applied to a pretrained convolutional neural network (CNN) trained on the MNIST dataset. The results demonstrate that our method enables easier identification of misclassified (anomalous) data points compared to category-based and random rendering orders.
WebGraphViz: A WebGL-Based Interactive Graph Visualization Tool for Retail Analytics
(The Eurographics Association, 2025) Aguilar, Luis Miguel; Al-Tarawneh, Ragaad; Humayoun, Shah Rukh; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
The growing volume and complexity of data from sources like social media, IoT systems, and security devices highlight the need for scalable, high-performance visualization tools. Traditional web technologies such as SVG and Canvas often struggle with large datasets, limiting interactivity. We present WebGraphViz, a WebGL-based graph visualization tool that leverages GPU parallelism to overcome these limitations. Performance evaluations across three interaction experiments in both high- and low-performance environments show that WebGraphViz significantly outperforms its SVG-based counterpart, enabling smooth exploration of large-scale graph data.
The Past Is All Around You: Augmenting Cultural Heritage On-Site
(The Eurographics Association, 2025) Passecker, Markus; Miksch, Silvia; Proksa, Franziska; Aigner, Wolfgang; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Digitized cultural heritage (CH) artifacts frequently lose their original immersive and historical context when presented through traditional digital means. Situated visualization, particularly through augmented reality (AR), offers a promising avenue to reconnect artifacts with their authentic physical environments. In this work, our objective is to explore methods for designing effective AR-based visualizations to enhance user engagement and understanding in cultural heritage contexts. We share initial insights derived from literature reviews, prototyping, and preliminary evaluations focusing on prominent Austrian CH sites.
Augmented Reality for Training in Small and Medium-Sized Manufacturing Companies
(The Eurographics Association, 2025) Ainin, Abdulla A.; Algabroun, Hatem; Linhares, Claudio D. G.; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
As manufacturing systems grow more complex, traditional 2D schematics and static models fall short in training and comprehension. This work explores Augmented Reality (AR) as a solution, enabling interactive and spatially meaningful learning experiences. Focusing on small and medium-sized enterprises (SMEs), the system integrates a Digital Twin (DT) with Microsoft HoloLens and Unity to provide immersive visualization and interaction with mechanical machines. Users can manipulate components through natural hand gestures, enhancing spatial reasoning and hands-on training. This mixed-reality approach offers SMEs a scalable and practical tool for modernizing workforce education in manufacturing.
Visual Analysis of Poker Hands for Individual Players
(The Eurographics Association, 2025) Dotzler, Konstantin Joachim; Agarwal, Shivam; Beck, Fabian; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Poker requires complex decisions and, to improve play, careful analysis. Typical analytics tools focus on individual hands, overlooking broader performance trends. This paper proposes an approach for both individual hand review and a more comprehensive gameplay analysis. The linked visualizations comprise a line chart for cumulative winnings, a scatterplot for winnings vs. hand rankings, a bar chart for detailed hand winnings, and an event sequence visualization of the selected hand.
Fairness-Aware Urban Planning in Sweden: An Interactive Visualization Tool for Equitable Cities
(The Eurographics Association, 2025) Othman, Reem; Powley, Benjamin; Martins, Rafael M.; Soares, Amilcar; Kerren, Andreas; Ferreira, Nivan; Linhares, Claudio D. G.; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
This study presents an interactive visualization tool that facilitates fairness-aware urban planning. The system introduces a fairness scale to assess the accessibility of potential new developments, using color-coded scatter plots to visualize disparities. An intuitive interaction design minimizes complexity while enhancing usability, enabling users to analyze urban infrastructure and services. Developed with web technologies, the tool leverages OpenStreetMap data to ensure adaptability across different cities. Future optimizations include advanced analytical capabilities and broader dataset integrations to improve decisionmaking in urban development.
Motivating Through Design: Affective Visualization in mHealth
(The Eurographics Association, 2025) Shin, Go-Un; Frankowska-Takhari, Sylwia; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Low retention and limited public health impact remain persistent challenges for mobile health (mHealth) apps [MAD*22]. This study explores affective visualization as a solution, advocating for a post-cognitivist perspective in defining the future of affective design. This approach proposes that technology should not tell users what or how to act or think but instead respond to how human minds work. This framework could transform user communication through visualization, fostering positive behavioural changes. The study also highlights affective visualization as a nascent yet promising domain for future research [LWC23].
Pie Chart Glyph Visualization of Uncertain Connected Components
(The Eurographics Association, 2025) Evers, Marina; Rasheed, Farhan; Masood, Talha Bin; Hotz, Ingrid; Weiskopf, Daniel; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
Edges of graphs are often associated with uncertainty. The inherent uncertainty of the data also induces uncertainty in derived graph attributes such as connected components. Even for planar graphs, visualizing the connected components in the graph embedding while encoding their uncertainty imposes challenges due to overlap. We present a visual encoding for uncertain connected components in a planar graph embedding. The underlying model does not require matching or assumptions on the overlap of the components and emphasizes uncertain boundary regions. We discuss different design options and show the applicability of our approach based on synthetic data and real-world data on force networks in granular materials.

Browse

Browsing EuroVisPosters2025 by Issue Date

Results Per Page

Sort Options