Design Principles for Comprehensible Process Discovery in Process Mining

Abstract

“Spaghetti-like” process models discovered through process mining are challenging to comprehend, especially, for inexperienced users. But, at the same time, they contain potential insights for decisionmakers. Designing process discovery techniques that work well in both aspects – being comprehensible and providing valuable information, for various data sets – is a challenging task in process mining. Therefore, we adopt metrics from various disciplines such as information theory, business process modelling, process mining, graph aesthetics, and cognitive load theory to define design principles for process discovery techniques in regards to model characteristics and visual layout principles. Each of the design principles includes a metric and reference value to ensure their testability and to provide quantitative orientation to designers. To assure that model comprehensibility does not come at the cost of losing essential information, we introduce an entropy-based measure as a boundary condition that expresses the amount of information a model encodes. We assess the effectiveness of the design principles in terms of their applicability in an experimental evaluation with synthetic and real-world event log data