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

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