Grouping Local Process Models
Viki Peeva Wil M. P. van der Aalst
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.