Data characterization for automatically visualizing heterogeneous information

Abstract

Automated graphical generation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information in static or interactive environments. When building such a system, it is important to thoroughly understand the presentation-related characteristics of domain-specific information. We define a data-analysis taxonomy that can be used to characterize heterogeneous information. In addition to capturing the presentation-related properties of data, our characterization takes into account the user's information-seeking goals and visual-interpretation preferences. We use automatically-generated examples from two different application domains to demonstrate the coverage of the proposed taxonomy and its utility for selecting effective graphical techniques.