What May Visualization Processes Optimize?

TL;DR

This paper introduces an information-theoretic framework for optimizing visualization processes by modeling data transformations as entropy reductions, validated through analysis of existing visualization workflows.

Contribution

It presents a novel abstract model and a cost-benefit measure for optimizing visualization workflows based on entropy reduction, supported by literature analysis.

Findings

Data transformations in visualization reduce entropy.

The measure explains advantages of successful visualization processes.

Framework applicable across different visualization levels.

Abstract

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.