Mining Frequent Patterns in Process Models

TL;DR

This paper introduces WoMine, an algorithm that extracts frequent behavioral patterns from process models, including complex structures, improving upon current techniques and validated with real-world models.

Contribution

WoMine is a novel algorithm capable of mining all types of process patterns, including sequences, choices, parallels, and loops, surpassing existing methods.

Findings

WoMine successfully extracts complex pattern structures.

It outperforms state-of-the-art techniques in pattern detection.

Validated on real-world process models from BPI Challenges.

Abstract

Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in well-structured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.