Using visual analytics to support decision making to solve the Kronos incident (VAST challenge 2014)
Fabian Fischer, Florian Stoffel, Sebastian Mittelstädt, Tobias Schreck, Daniel A. Keim · 2014
Gaining insights from different heterogeneous data sources is one of the biggest challenges in decision making support. The large volumes of data can only be combined by sophisticated automatic methods. However, unexpected patterns can only be identified with the help of human intuition. In this paper, we present our visual analytics work-flows and tools to process heterogeneous data such as social networks, text streams, and geo-temporal data. We apply these tools on the VAST Challenge data and present our findings and assumptions that we identified in our analysis.