A03 | Quantification of Visual Analytics Transformations and Mappings

Prof. Daniel A. Keim, University of Konstanz
Email | Website

Daniel Keim

Prof. Michael Sedlmair, University of Stuttgart
Email | Website

Michael Sedlmair

Matthias Kraus, University of Konstanz (until May 2021) – Email  |  Website

Daniel Seebacher, University of Konstanz (from July 2021) – Email  |  Website

Frederik Dennig, University of Konstanz (from August 2021) – Email  |  Website

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: Map visualization with original (top) and transformed (bottom) data.
Adapted from: Schneidewind et al. "Pixnostics: Towards measuring the value of visualization." VAST, 2006.

Fig. 2: Optimized ordering of dimensions in a Parallel Coordinate Plot to support cluster and correlation analysis.
Adapted from: Johansson & Johansson. "Interactive dimensionality reduction through user-defined combinations of quality metrics." Visualization and Computer Graphics, 2009.


  1. 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, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14002.
  2. 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, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14000.
  3. M. Kraus et al., “Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020, pp. 546:1–546:14. doi: 10.1145/3313831.3376675.
  4. M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Universität Konstanz, Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
  5. 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, no. 1, Art. no. 1, 2020, doi: doi:10.2196/13191.
  6. 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, no. 8, Art. no. 8, 2020, doi: 10.3390/brainsci10080537.
  7. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 -- 2019),” 2020. doi: doi: 10.1109/ISMAR50242.2020.00069.
  8. F. L. Dennig, T. Polk, Z. Lin, T. Schreck, H. Pfister, and M. Behrisch, “FDive: Learning Relevance Models using Pattern-based Similarity Measures,” Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), 2019, doi: 10.1109/VAST47406.2019.8986940.
  9. 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,” in Proceedings of the IEEE Visualization Conference (VIS), 2019, pp. 86–90. doi: 10.1109/VISUAL.2019.8933706.
  10. C. Schätzle, F. L. Denning, M. Blumenschein, D. A. Keim, and M. Butt, “Visualizing Linguistic Change as Dimension Interactions,” in Proceedings of the International Workshop on Computational Approaches to Historical Language Change, 2019, pp. 272–278. doi: 10.18653/v1/W19-4734.
  11. M. Miller, X. Zhang, J. Fuchs, and M. Blumenschein, “Evaluating Ordering Strategies of Star Glyph Axes,” in Proceedings of the IEEE Visualization Conference (VIS), 2019, pp. 91–95. doi: 10.1109/VISUAL.2019.8933656.
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  13. 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.
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  16. 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, vol. 3, pp. 164–175. doi: http://dx.doi.org/10.5220/0006265101640175.
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  20. D. Jäckle, M. Hund, M. Behrisch, D. A. Keim, and T. Schreck, “Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), 2017, pp. 1–12. doi: 10.1109/VAST.2017.8585613.
  21. M. Hund et al., “Visual Analytics for Concept Exploration in Subspaces of Patient Groups,” Brain Informatics, vol. 3, no. 4, Art. no. 4, 2016, doi: 10.1007/s40708-016-0043-5.
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