Information Aware Uncertainty Visualization

• Applicant: Professor Dr.-Ing. Holger Theisel
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 557417077

Scientific data sets are growing in size and complexity. One well-established way of data analysis is visualization which complements and competes with a number of automatic analysis techniques. The problem of growing data complexity becomes even worse: modern data acquisition comes with uncertainty information; its analysis is even more important than the pure data analysis because it gives information about the reliability of found data properties. Additional uncertainty information increases data size by at least one more scale, making uncertainty visualization techniques more involved, complex, and hard to interpret. We propose a fundamentally new approach in uncertainty visualization. Instead of making the visualizations more complex, the additional consideration of uncertainty should simplify the visualization while still showing all relevant data properties. At first glance, this sounds counter-intuitive because uncertainty information comes with data growing. However, the approach is based the uncertainty paradox in visualization which is systematically investigated and exploited up to the development of concrete new visualization techniques. The so developed information aware uncertainty visualization techniques are applied to data sets from medical imaging, climate research, mechanical engineering and CFD.

• DFG Programme: Research Grants