A03 | Quantification of Visual Explainability
High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult problem. This project aims at advancing the field of quality-metric-driven data visualization with the central research question of how to quantify the quality of transformations and mappings of high-dimensional data for visual analytics.
Research Questions
How can we measure and quantify the quality of a visualization? In which way do methods in the data space differ from methods in the image space?
How can we compare the measured quality of a visualization with the perception of a human?
How can the user be involved into a quality-metric-driven process of visual mappings and transformations?
What is the influence of perceptual effects on quality measures? Can we enhance the visual representation of information by introducing perceptual effects into visualizations?
Fig. 1: Comparison of regular parallel coordinates with our slope-dependent polyline rendering. Parallel coordinates face two problems: Diagonal lines are rendered more closely (a), and zig-zag patterns are perceived as clusters, although there are no such clusters in the data (c). We render each line segment based on its slope between two axes. Cluster density is not distorted (b), and the zig-zag pattern effect is reduced (d).
Fig. 2: Comparison of similarity (SIM) and dissimilarity (DIS) based ordering using identical data records. Our results show that users perform better when the glyphs represent salient shapes and spikes, which is achieved by a dissimilarity-based ordering of the dimensions.
Publications
- L. Joos, D. A. Keim, and M. T. Fischer, “Leveraging LLMs for semi-automatic corpus filtration in systematic literature reviews,” Computers & Graphics, vol. 135, p. 104537, 2026, doi: 10.1016/j.cag.2026.104537.
- D. Blumberg, P. Paetzold, M. Stroh, O. Deussen, D. A. Keim, and F. L. Dennig, “FluidMap: Proportional and Spatially Consistent Layout Enrichments in Multidimensional Projections,” Computer Graphics Forum, 2026, doi: 10.1111/cgf.70293.
- L. Joos et al., “Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings,” Journal of Graph Algorithms and Applications, vol. 30, Art. no. 2, 2026, doi: 10.7155/jgaa.v30i2.3102.
- L. Joos, D. A. Keim, and M. T. Fischer, “Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews,” in EuroVis Workshop on Visual Analytics (EuroVA), 2025. doi: 10.2312/eurova.20251105.
- J. Fuchs, F. L. Dennig, M.-V. Heinle, D. A. Keim, and S. Di Bartolomeo, “Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis,” Computer Graphics Forum, vol. 43, Art. no. 3, 2024, doi: 10.1111/cgf.15079.
- L. Joos et al., “Evaluating Node Selection Techniques for Network Visualizations in Virtual Reality,” in ACM Symposium on Spatial User Interaction, New York, NY, USA: ACM, 2024, pp. 1–11. doi: 10.1145/3677386.3682102.
- R. Buchmüller, B. Jäckl, M. Behrisch, D. A. Keim, and F. L. Dennig, “cPro: Circular Projections Using Gradient Descent,” in Proceedings of the 15th International EuroVis Workshop on Visual Analytics (EuroVA), The Eurographics Association, 2024. doi: 10.2312/eurova.20241111.
- D. Blumberg, Y. Wang, A. Telea, D. A. Keim, and F. L. Dennig, “Inverting Multidimensional Scaling Projections Using Data Point Multilateration,” in Proceedings of the 15th International EuroVis Workshop on Visual Analytics (EuroVA), The Eurographics Association, 2024. doi: 10.2312/eurova.20241112.
- F. L. Dennig et al., “The Categorical Data Map: A Multidimensional Scaling-Based Approach,” in 2024 IEEE Visualization in Data Science (VDS), IEEE, 2024, pp. 25–34. doi: 10.1109/vds63897.2024.00008.
- L. Joos, B. Jäckl, D. A. Keim, M. T. Fischer, L. Peska, and J. Lokoč, “Known-Item Search in Video: An Eye Tracking-Based Study,” in Proceedings of the 2024 International Conference on Multimedia Retrieval (ICMR ’24), New York, NY, USA: ACM, 2024, pp. 311–319. doi: 10.1145/3652583.3658119.
- F. L. Dennig, M. Miller, D. A. Keim, and M. El-Assady, “FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, Art. no. 8, 2023, doi: 10.1109/tvcg.2023.3288356.
- M. Kraus et al., “Immersive Analytics with Abstract 3D Visualizations: A Survey,” Computer Graphics Forum, Art. no. 41, 2021, doi: 10.1111/cgf.14430.
- F. L. Dennig, M. T. Fischer, M. Blumenschein, J. Fuchs, D. A. Keim, and E. Dimara, “ParSetgnostics: Quality Metrics for Parallel Sets,” Computer Graphics Forum, vol. 40, Art. no. 3, 2021, doi: 10.1111/cgf.14314.
- M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
- D. R. Wahl et al., “Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment,” JMIR mHealth and uHealth, vol. 8, Art. no. 1, 2020, doi: 10.2196/13191.
- M. Blumenschein, L. J. Debbeler, N. C. Lages, B. Renner, D. A. Keim, and M. El-Assady, “v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions,” Computer Graphics Forum, vol. 39, Art. no. 3, 2020, doi: 10.1111/cgf14002.
- M. Blumenschein, X. Zhang, D. Pomerenke, D. A. Keim, and J. Fuchs, “Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates,” Computer Graphics Forum, vol. 39, Art. no. 3, 2020, doi: 10.1111/cgf.14000.
- D. Schubring, M. Kraus, C. Stolz, N. Weiler, D. A. Keim, and H. Schupp, “Virtual Reality Potentiates Emotion and Task Effects of Alpha/Beta Brain Oscillations,” Brain Sciences, vol. 10, Art. no. 8, 2020, doi: 10.3390/brainsci10080537.
- C. Schätzle, F. L. Dennig, M. Blumenschein, D. A. Keim, and M. Butt, “Visualizing Linguistic Change as Dimension Interactions.” Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 272–278, 2019. doi: 10.18653/v1/w19-4734.
- D. Pomerenke, F. L. Dennig, D. A. Keim, J. Fuchs, and M. Blumenschein, “Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters.” IEEE, pp. 86–90, 2019. doi: 10.1109/visual.2019.8933706.
- F. L. Dennig, T. Polk, Z. Lin, T. Schreck, H. Pfister, and M. Behrisch, “FDive: Learning Relevance Models Using Pattern-based Similarity Measures.” IEEE, pp. 69–80, 2019. doi: 10.1109/vast47406.2019.8986940.
- M. Miller, X. Zhang, J. Fuchs, and M. Blumenschein, “Evaluating Ordering Strategies of Star Glyph Axes.” IEEE, pp. 91–95, 2019. doi: 10.1109/visual.2019.8933656.
- D. Sacha et al., “SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, doi: 10.1109/tvcg.2017.2744805.
- L. J. Debbeler, M. Gamp, M. Blumenschein, D. A. Keim, and B. Renner, “Polarized But Illusory Beliefs About Tap and Bottled Water: A Product- and Consumer-Oriented Survey and Blind Tasting Experiment,” Science of the Total Environment, vol. 643, pp. 1400–1410, 2018, doi: 10.1016/j.scitotenv.2018.06.190.
- M. Blumenschein et al., “SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach.” IEEE, pp. 36–47, 2018. doi: 10.1109/vast.2018.8802486.
- D. Jäckle, M. Hund, M. Behrisch, D. A. Keim, and T. Schreck, “Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces.” IEEE, pp. 1–12, 2017. doi: 10.1109/vast.2017.8585613.
- M. Stein et al., “Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2017, doi: 10.1109/tvcg.2017.2745181.
- D. Jäckle, F. Stoffel, S. Mittelstädt, D. A. Keim, and H. Reiterer, “Interpretation of Dimensionally-Reduced Crime Data: A Study with Untrained Domain Experts,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2017, pp. 164–175. [Online]. Available: https://bib.dbvis.de/publications/details/697
- M. Behrisch et al., “Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, [Online]. Available: https://ieeexplore.ieee.org/document/7534849
- M. Hund et al., “Visual Quality Assessment of Subspace Clusterings,” in Proceedings of the KDD Workshop on Interactive Data Exploration and Analytics (IDEA), I. KDD 2016, Ed., 2016, pp. 53–62.
Project Group A
Models and Measures
Completed
Project Group B
Adaptive Algorithms
Completed
Project Group C
Interaction
Completed
Project Group D
Applications
Completed
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© SFB-TRR 161 | Quantitative Methods for Visual Computing | 2019.