Standing on the Shoulders of Giants: An Empirical Lens on Process Model Comprehensibility

Abstract

Process mining relies on visual representations to support the exploration and interpretation of event data. However, understanding these representations is not always straightforward. Insights from related domains such as process modeling can help explain how users interpret process models and which factors influence their understanding. This paper presents a conceptual framework that organizes the available empirical evidence on process model comprehensibility. It contains the factors affecting comprehensibility and links them to their effects on process model comprehensibility indicators related to performance, subjective evaluation, and cognitive load. Factors are grouped into six conceptual: interactivity and animation, modeling languages, model features, personal features, model medium, and task features. The framework highlights both well-researched and underexplored areas, including the relatively limited empirical attention to interactivity, which plays a growing role in process mining but has received limited attention so far. This paper thereby offers a solid basis for further research into how users interpret and interact with increasingly complex process representations.