Big data does not only reach the limits of predictability but above all comprehensibility. Three main areas of Visual Analytics are explored to facilitate a faster understanding. On the one hand, methods are developed that integrate the user deeper into the visualization. For this purpose, novel immersive interaction methods in front of large display walls, as well as techniques of virtual and augmented reality are investigated.
On the other hand, semi-automatic methods will be developed, which support the user in the interaction by adapting filter and other visualization parameters. In addition strategies should be implemented in order to divide the data into easier-to-understand parts, especially in the area of segmentation. Methods of machine learning will be used to achieve this.
The third focus is the development of adapted hierarchical visualizations in the field of life sciences. Again, coping with large amounts of data is a major challenge. For this reason, the promising and already successfully used tools should be expanded into a multi-level visualization and interaction workflow. The gained insights are integrated into a better understandable visual analysis and the workflow is extended by new data types.