Grouping Local Process Models
This work addresses a specific bottleneck in process mining for organizations dealing with unstructured processes, offering an incremental solution to reduce redundancy in discovered models.
The paper tackles the problem of model explosion and repetition in Local Process Model (LPM) discovery for unstructured processes by proposing a three-step pipeline to group similar LPMs using various similarity measures, demonstrating its usefulness in a real-life case study and analyzing improvements on multiple real event logs.
In recent years, process mining emerged as a proven technology to analyze and improve operational processes. An expanding range of organizations using process mining in their daily operation brings a broader spectrum of processes to be analyzed. Some of these processes are highly unstructured, making it difficult for traditional process discovery approaches to discover a start-to-end model describing the entire process. Therefore, the subdiscipline of Local Process Model (LPM) discovery tries to build a set of LPMs, i.e., smaller models that explain sub-behaviors of the process. However, like other pattern mining approaches, LPM discovery algorithms also face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of models, and subsets of them are close in structure or behavior. This work proposes a three-step pipeline for grouping similar LPMs using various process model similarity measures. We demonstrate the usefulness of grouping through a real-life case study, and analyze the impact of different measures, the gravity of repetition in the discovered LPMs, and how it improves after grouping on multiple real event logs.