A03 | Quantification of Visual Analytics Transformations and Mappings

Prof. Daniel A. Keim, Universität Konstanz
Email | Website

Daniel Keim

Prof. Daniel Weiskopf, Universität Stuttgart
Email | Website

Daniel Weiskopf

Michael Blumenschein (formerly: Hund), Universität Konstanz – 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?

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

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.

Publications

  1. Sacha, D.; Kraus, M.; Bernard, J.; u. a. (2017): SOMFlow: Guided exploratory cluster analysis with self-organizing maps and analytic provenance. IEEE Conference on Visual Analytics Science and Technology. o.V. (IEEE Conference on Visual Analytics Science and Technology).
  2. Hund, Michael; Böhm, Dominic; Sturm, Werner; u. a. (2016a): „Visual analytics for concept exploration in subspaces of patient groups.“. In: Brain Informatics. (Brain Informatics) 3 (4), S. 233–247.
  3. Behrisch, M.; Bach, B.; Hund, M.; u. a. (2016): „Magnostics: Image-based Search of Interesting Matrix Views for Guided Network Exploration.“. In: Society, IEEE Computer (Hrsg.), S. 99.
  4. Jäckle, D.; Stoffel, F.; Mittelstädt, S.; u. a. (2017): „Interpretation of Dimensionally-Reduced Crime Data: A Study with Untrained Domain Experts.“. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging; Theory, Computer Graphics; 2017), Applications (VISIGRAPP (Hrsg.), S. 164–175.
  5. Hund, Michael; Färber, Ines; Behrisch, Michael; u. a. (2016b): „Visual Quality Assessment of Subspace Clusterings“. In: KDD 2016, IDEA (Hrsg.).
  6. Hund, Michael; Behrisch, Michael; Farber, Ines; u. a. (2015): „Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion“. In: 8th International Conference, SISAP 2015 (Hrsg.).

...