# A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs

arXiv: https://arxiv.org/abs/2201.03740 · HTML: https://mlsanity.com/paper/2201.03740/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation](https://mlsanity.com/paper/2404.12056/) (2024)
- [TaleFrame: An Interactive Story Generation System with Fine-Grained Control and Large Language Models](https://mlsanity.com/paper/2512.02402/) (2025)
- [From Interaction to Intent: Inferring User Objectives from Provenance Logs](https://mlsanity.com/paper/2607.04501/) (2026)
- [WidgetVA: A Widget-Centric Framework and Benchmark for Agentic Visual Analytics](https://mlsanity.com/paper/2609.24094/) (2026)

## Abstract (arXiv)

Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their higher-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to facilitate this mapping process, no formal methods exist for systematically applying these existing theories to user interaction logs. This paper seeks to bridge the gap between visualization task taxonomies and interaction log data by making the taxonomies more actionable for interaction log analysis. To achieve this, we leverage structural parallels between how people express themselves through interactions and language by reformulating existing theories as regular grammars. We represent interactions as terminals within a regular grammar, similar to the role of individual words in a language, and patterns of interactions or non-terminals as regular expressions over these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven visualization taxonomies and develop code to apply them to three interaction log datasets. In analyzing our results, we find that existing taxonomies at the low-level (i.e., terminals) show mixed results in expressing multiple interaction log datasets, and taxonomies at the high-level (i.e., regular expressions) have limited expressiveness, due to primarily two challenges: inconsistencies in interaction log dataset granularity and structure, and under-expressiveness of certain terminals. Based on our findings, we suggest new research directions for the visualization community for augmenting existing taxonomies, developing new ones, and building better interaction log recording processes to facilitate the data-driven development of user behavior taxonomies.