A03 | Quantification of Visual Analytics Transformations and Mappings

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

Daniel Keim

Jun.-Prof. Michael Sedlmair, University of Stuttgart
Email | Website

Michael Sedlmair

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.


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