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Abstract Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their high‐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 asregular grammars.We represent interactions asterminalswithin a regular grammar, similar to the role of individual words in a language, and patterns of interactions ornon‐terminalsasregular expressionsover these terminals to capture common language patterns. To demonstrate our approach, we generate regular grammars for seven existing visualization taxonomies and develop code to apply them to three public interaction log datasets. In analyzing these regular grammars, we find that the 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 to augment existing taxonomies, develop new ones, and build better interaction log recording processes to facilitate the data‐driven development of user behavior taxonomies.
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This content will become publicly available on February 1, 2026
The State of the Art in User‐Adaptive Visualizations
Abstract Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user‐adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub‐domains of the visualization community. A user‐adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user‐adaptive visualizations into five components: Capture ⒶInputfrom the user and any relevant peripheral information. Perform computational ⒷUser Modellingwith this input to construct a ⒸUser Representation. Employ ⒹAdaptation Assignmentlogic to identify when and how to introduce ⒺInterventions. Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work.
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- PAR ID:
- 10589161
- Publisher / Repository:
- COMPUTER GRAPHICS forum
- Date Published:
- Journal Name:
- Computer Graphics Forum
- Volume:
- 44
- Issue:
- 1
- ISSN:
- 0167-7055
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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