Capturing and Reusing Empirical Visualization Knowledge

The context-aware discovery and ranking of visualization components is a crucial part of an adaptive, automated information visualization system. Since existing approaches allow for using expert knowledge formalized a priori, insights gained during the visualization processes by the users, e. g., suitable data-visualization combinations, are mostly neglected. In this paper, we propose a concept to capture and formalize these insights. Furthermore, we enhance a context-aware ranking approach using this knowledge by applying the well-known collaborative filtering. Thus, we are able to employ ratings also if data-visualization combinations are new for the current user.