Information Theoretic Measures for Visual Analytics

In this paper, we argue that information theoretic measures may provide a robust, broadly applicable, repeatable metric to assess how a system enables people to reduce high-dimensional data into topically relevant subsets of information. Explosive growth in electronic data necessitates the development of systems that balance automation with human cognitive engagement to facilitate pattern discovery, analysis and characterization, variously described as \"cognitive augmentation\" or \"insight generation.\" However, operationalizing the concept of insight in any measurable way remains a difficult challenge for visualization researchers. The \"golden ticket\" of insight evaluation would be a precise, generalizable, repeatable, and ecologically valid metric that indicates the relative utility of a system in heightening cognitive performance or facilitating insights. Unfortunately, the golden ticket does not yet exist. In its place, we are exploring information theoretic measures derived from Shannon's ideas about information and entropy as a starting point for precise, repeatable, and generalizable approaches for evaluating analytic tools. We are specifically concerned with needle-in-haystack workflows that require interactive search, classification, and reduction of very large heterogeneous datasets into manageable, task-relevant subsets of information. We assert that systems aimed at facilitating pattern discovery, characterization and analysis -- i.e., \"insight\" - must afford an efficient means of sorting the needles from the chaff; and simple compressibility measures provide a way of tracking changes in information content as people shape meaning from data.