A03 | Quantification of Visual Analytics Transformations and Mappings
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
- M. Hund et al., “Visual analytics for concept exploration in subspaces of patient groups.,” Brain Informatics, vol. 3, no. 4, pp. 233–247, 2016.
- M. Behrisch et al., “Magnostics: Image-based Search of Interesting Matrix Views for Guided Network Exploration.,” 2016, vol. 23, no. 1–1, p. 99.
- 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 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Best Student Paper Award, 2017, no. 3, pp. 164–175.
- D. Sacha et al., “SOMFlow: Guided exploratory cluster analysis with self-organizing maps and analytic provenance,” IEEE Conference on Visual Analytics Science and Technology, 2017.
- C. Schulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques.,” in BELIV Workshop 2016, 2016, pp. 112--124.
- C. Schätzle, M. Hund, F. L. Dennig, M. Butt, and D. A. Keim, “HistoBankVis: Detecting Language Change via Data Visualization.,” in Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language, 2017, no. 133, pp. 32–39.
- 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,” 2018.
- M. Behrisch et al., “Quality Metrics for Information Visualization.,” EuroVis STAR, 2018.
- M. Hund et al., “Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion,” in Similarity Search and Applications, vol. 1, no. 9371, G. Amato, R. Connor, F. Falchi, and C. Gennaro, Eds. Springer International Publishing, 2015, pp. 307–313.
- L. Merino et al., “On the Impact of the Medium in the Effectiveness of 3D Software Visualizations,” in VISSOFT’17: Proceedings of the 5th IEEE Working Conference on Software Visualization, 2017.
- M. Hund et al., “Visual Quality Assessment of Subspace Clusterings,” in KDD 2016 Interactive Data Exploration and Analytics (IDEA), 2016.
- M. Stein et al., “Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis.,” in IEEE Transactions on Visualization and Computer Graphics (Proceedings of the Visual Analytics Science and Technology), 2017.
- D. Jäckle, M. Hund, M. Behrisch, D. A. Keim, and T. Schreck, “Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,” in IEEE Conference on Visual Analytics Science and Technology (VAST), 2017.
Project Group A
Models and Measures
Project Group B
Adaptive Algorithms
Project Group C
Interaction
Project Group D
Applications
FOR SCIENTISTS
FOR PUPILS
PRESS AND MEDIA
SOCIAL MEDIA
© SFB-TRR 161 | Quantitative Methods for Visual Computing | 2017.