The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of visual discovery, as they lack full provenance information and context. Videos are difficult to produce and edit, particularly due to the nonlinear nature of the exploration process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this talk, I will introduce our efforts to more tightly integrate interactive data exploration with the presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author 'Vistories', visual stories based on the history of the exploration. These Vistories can be shared for others to view, but also to retrace and extend the original analysis. I will demonstrate how such methods can increase the reproducibility of cancer research and drug discovery. I will also present a solution for quickly retrieving visual analysis states from provenance graphs that are too large to be visualized in a meaningful way. Finally, I will underscore the potential of Vistories for web-based notebook environments (e.g., Jupyter Notebook) and machine learning by showing early results from ongoing projects.

Bio:
Marc Streit is a tenured Full Professor at the Institute of Computer Graphics at Johannes Kepler University Linz where he leads the Visual Data Science group. He finished his PhD at the Institute for Computer Graphics and Vision at Graz University of Technology in early 2011 and moved to Linz later that year. In 2012 he was a visiting researcher at the Center for Biomedical Informatics (CBMI) at Harvard Medical School. As part of a Fulbright scholarship for research and lecturing he was a visiting professor at the Visual Computing Group at Harvard Paulson School in 2014. Marc is also a guest lecturer at the Imperial College Business School.

His scientific areas of interest include visualization, visual analytics, and biological data visualization, where he is particularly interested in the integrated analysis of large heterogeneous data. Together with his team he develops novel visual analysis tools for cancer research, drug discovery, and other biomedical applications. Since 2016 Marc is also the CEO of the JKU spin-off company datavisyn.