What May Visualization Processes Optimize?
This work addresses the challenge of systematically optimizing visualization workflows for data analysts and researchers, though it appears incremental as it builds on existing concepts with a new theoretical framework.
The paper tackles the problem of optimizing visualization processes by proposing an information-theoretic measure based on data space transformations and entropy reduction, and demonstrates its validity by explaining advantages of successful visualization processes in the literature.
In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.