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

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