A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation
This work addresses a theoretical issue in visualization research by refining measurement tools, but it is incremental as it builds on prior information theory frameworks without immediate practical application.
The paper tackled the problem of an unbounded term in existing cost-benefit measures for visualization, proposing to replace it with bounded alternatives including a new divergence measure, and conducted a conceptual evaluation to narrow down options with better mathematical properties.
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. As the first part of comparative evaluation, we use visual analysis to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this paper provide the basis for further comparative evaluation through synthetic and experimental case studies, which are to be reported in a separate paper.