Visual Data Analysis with Task-based Recommendations

TL;DR

TaskVis is a task-oriented visualization recommendation system that enables precise user task selection, providing targeted visualizations through rule-based enumeration, ranking, and combination strategies, validated by use cases and user studies.

Contribution

Introduces TaskVis, a novel task-based visualization recommendation system with a rule-based approach, ranking schemes, and combination strategies for targeted data analysis.

Findings

Effective task-based visualization recommendations

Enhanced ranking and combination strategies

Validated through use cases and user study

Abstract

General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.