Defining Meaningful Local Process Models

Brunings, M., Fahland, D., & van Dongen, B. (2022). Defining Meaningful Local Process Models. In M. Koutny, F. Kordon, & D. Moldt (Eds.), Transactions on Petri Nets and Other Models of Concurrency XVI (pp. 24-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13220 LNCS). Springer.


Current process discovery techniques are unable to produce meaningful models for semi-structured processes, as they are either too inaccurate or too complex. In this paper we use the idea of local process models (LPMs) to model fragments of a semi-structured process and explore the potential of sets of LPMs. Automatic LPM discovery finds many small patterns but doesn’t find patterns larger than 4–5 events, it produces too many models, and the discovered models describe some events from the log multiple times while leaving others unexplained. We manually construct a set of LPMs for the well-known BPIC12 event log that (1) contains a small number of models that (2) have high accuracy measures such as fitness and precision while (3) they together cover the whole event log and (4) do not cover parts of the log multiple times unnecessarily. We find that existing evaluation techniques for LPMs do not work for sets of LPMs and we propose several measures that help determine the quality of a set of LPMs both as a whole and as individual LPMs. We show that sets of LPMs can indeed be used to model semi-structured processes by not thinking of such processes as monolithic, but rather a collection of smaller processes working together.

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