Browsing by Author "Scheibel, Willy"

Now showing 1 - 8 of 8
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    Constructing Hierarchical Continuity in Hilbert & Moore Treemaps
    (The Eurographics Association, 2023) Scheibel, Willy; Döllner, Jürgen; Gillmann, Christina; Krone, Michael; Lenti, Simone
    The Hilbert and Moore treemap layout algorithms are based on the space-filling Hilbert and Moore curves, respectively, to map tree-structured datasets to a 2D treemap layout. Considering multiple snapshots of a time-variant dataset, one of the design goals for Hilbert and Moore treemaps is layout stability, i.e., low changes in the layout for low changes in the underlying tree-structured data. For this, their underlying space-filling curve is expected to be continuous across all nodes and hierarchy levels, which has to be considered throughout the layouting process. We propose optimizations to subdivision templates, their orientation, and discuss the continuity of the underlying space-filling curve. We show real-world examples of Hilbert and Moore treemaps for small and large datasets with continuous space-filling curves, allowing for improved layout stability.
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    A Dashboard for Interactive Convolutional Neural Network Training And Validation Through Saliency Maps
    (The Eurographics Association, 2023) Cech, Tim; Simsek, Furkan; Scheibel, Willy; Döllner, Jürgen; Gillmann, Christina; Krone, Michael; Lenti, Simone
    Quali-quantitative methods provide ways for interrogating Convolutional Neural Networks (CNN). For it, we propose a dashboard using a quali-quantitative method based on quantitative metrics and saliency maps. By those means, a user can discover patterns during the training of a CNN. With this, they can adapt the training hyperparameters of the model, obtaining a CNN that learned patterns desired by the user. Furthermore, they neglect CNNs which learned undesirable patterns. This improves users' agency over the model training process.
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    A Dashboard for Simplifying Machine Learning Models using Feature Importances and Spurious Correlation Analysis
    (The Eurographics Association, 2024) Cech, Tim; Kohlros, Erik; Scheibel, Willy; Döllner, Jürgen; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
    Machine Learning models underlie a trade-off between accurracy and explainability. Given a trained, complex model, we contribute a dashboard that supports the process to derive more explainable models, here: Fast-and-Frugal Trees, with further introspection using feature importances and spurious correlation analyses. The dashboard further allows to iterate over the feature selection and assess the trees' performance in comparison to the complex model.
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    Discussion and Showcase of an Implementation of Task Taxonomies in Visualizations Based on the Dust and Magnet Metaphor
    (The Eurographics Association, 2025) Böttger, Florence; Scheibel, Willy; Döllner, Jürgen; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
    In order to visualize multivariate data, it is often necessary to reduce their dimensionality. Visualizations based on the Dust and Magnet metaphor aim to present the information of the reduced dimensions via interactivity, while also supporting user comprehension by using a metaphor of ferrous dust and magnets. We compare two approaches to implementing this metaphor into a visualization tool along a set of core visualization tasks and propose extensions to make such visualizations more comprehensible and less misleading.
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    Exploring High-Dimensional Data by Pointwise Filtering of Low-Dimensional Embeddings
    (The Eurographics Association, 2024) Atzberger, Daniel; Jobst, Adrian; Scheibel, Willy; Döllner, Jürgen; Hunter, David; Slingsby, Aidan
    Dimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset while preserving local and global structures. By representing a high-dimensional dataset as a twodimensional scatterplot, a user can explore structures within the dataset. However, dimensionality reductions inherit distortions that might result in false deductions. This work presents a visualization approach that combines a two-dimensional scatterplot derived from a dimensionality reduction with two pointwise filtering possibilities. Each point is associated with two pointwise metrics that quantify the correctness of its neighborhood and similarity to surrounding data points. By setting threshold for these two metrics, the user is supported in several scatterplot analytics tasks, e.g., class separation and outlier detection. We apply our visualization to a text corpus to detect interesting data points visually and discuss the findings.
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    A Graph Layout Evaluation System for Communication Graphs
    (The Eurographics Association, 2025) Schröter, Valentin; Scheibel, Willy; Döllner, Jürgen; Diehl, Alexandra; Kucher, Kostiantyn; Médoc, Nicolas
    Communication networks are a type of graph that occurs in different areas such as robotic, Internet of Things, and general network communication. Layouting such networks for visualization is an ongoing research task, while already existing layout algorithms are plenty and allow for broad parameterization, making the choice of a fitting algorithm difficult. We propose a graph layout evaluation system where a user - a researcher or visualization designer - can upload own graphs, select preloaded ones, or generate synthetic graphs to explore different layouts through their generated layouts. The system allows for configuration of multiple layouts that can then be explored by the user using side-by-side comparison and an enlarged view. Further, the system allows for computation of layout metrics. The overview of different layouts and associated layout metrics can then be used to select a fitting algorithm. The system is prepared for further layout algorithms and layout quality metrics, building a starting point for graph layout research.
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    Interactive Human-guided Dimensionality Reduction using Landmark Positioning
    (The Eurographics Association, 2024) Cech, Tim; Raue, Christian; Sadrieh, Frederic; Scheibel, Willy; Döllner, Jürgen; Kucher, Kostiantyn; Diehl, Alexandra; Gillmann, Christina
    Dimensionality Reduction Techniques (DRs) are used for projecting high-dimensional data onto a two-dimensional plane. One subclass of DRs are such techniques that utilize landmarks. Landmarks are a subset of the original data space that are projected by a slow and more precise technique. The other data points are then placed in relation to these landmarks with respect to their distance in the high-dimensional space. We propose a technique to refine the placement of the landmarks by a human user. We test two different techniques for unprojecting the movement of the low-dimensional landmarks into the high-dimensional data space. We showcase that such a movement can increase certain quality metrics while decreasing others. Therefore, users may use our technique to challenge their understanding of the high-dimensional data space.
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    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.