Abstract: Visual Analytics (VA) is a paradigm for insight generation and automated reasoning by transforming data into hypotheses and visualization to extract new insights, feeding them back into the data.
Many applications use this principle to provide meaningful mechanisms to assist decision-makers in achieving their goals. This process can be affected by a variety of uncertainties that can interfere with the users decision-making process. Unfortunately, there is no methodical description and handling tool to systematically include uncertainty in VA. We introduce uncertainty-aware viual analytics and its'systematic construction to solve this issue. Further, we present success stories from biomedical applications where UAVA is utilized.
CV: Christina Gillmann is a researcher with the Signal and Image Processing Group, University of Leipzig, Germany, leading her own subgroup on uncertaintyaware visual analytics (UAVA). Her research interests include UAVA, medical visualization, uncertainty analysis, and the transferability of visualization approaches into applications. She received the Ph.D. degree in computer science from the University of Kaiserslautern, Germany, in 2018.
45 + 15
Host: Dr. Gröller, Eduard
Institute of Visual Computing & Human-Centered Technology
Favoritenstr. 9-11 / E193-02
Austria - Europe