# What May Visualization Processes Optimize?

arXiv: https://arxiv.org/abs/1506.02245 · HTML: https://mlsanity.com/paper/1506.02245/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2015.

## Built on by (most cited first, 7 of 7)

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- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [ATWL: A Formal Language for Representing, Comparing, and Reusing Visual Analytics Workflows](https://mlsanity.com/paper/2605.25489/) (2026)
- [The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality](https://mlsanity.com/paper/2603.16873/) (2026)
- [The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems](https://mlsanity.com/paper/2407.05341/) (2024)
- [Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics](https://mlsanity.com/paper/2607.00969/) (2026)

## Abstract (arXiv)

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.