- 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?
SMARTexplore: A novel, table-based Visual Analytics approach to identify and understand clusters, correlations, and complex patterns in high-dimensional data. We use quality metrics for a semi-automatic reliability analysis and a pattern-based layout of rows and columns. Pattern matching and subspace analysis algorithms are used to reveal interesting findings. Try it out here.
This research project received funding from the DFG (Deutsche Forschungsgemeinschaft) within the SFB-TRR 161.