Visualizing repetition in process execution variants from partially ordered event data

Operational processes often exhibit concurrency, where the execution of activities can overlap in time. Moreover, repetitions of activities, both intentional (e.g., iterative tasks) and unintentional (e.g., rework) often occur. Existing process mining techniques and visualizations largely assume sequential event data, making it difficult to analyze repetitions in partially ordered event data, which better captures real-world process behavior. We address this gap by introducing a novel arc-diagram-based visualization that highlights recurring activity patterns within individual process execution variants. This approach allows analysts to intuitively detect repetitions that are otherwise obscured in raw data or traditional variant views. We validate the usefulness and ease of use of the proposed visualization through a user study with process mining experts and provide an implementation of our contribution in an open-source tool, supporting practical adoption. • Novel visualization technique for identifying repetition patterns within process execution variants. • Designed for partially ordered event data, revealing repetition patterns hidden in purely sequential views. • User evaluation indicating the usefulness of the visualization in identifying repetition patterns and its ease of use.