Mining Frequent Patterns in Process Models
This work addresses the limitation in process mining for organizations by enabling better information extraction from complex or well-structured process models, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of extracting frequent behavioral patterns from process models, which current techniques cannot fully capture, and presents WoMine, an algorithm that successfully finds all types of patterns, including sequences, selections, parallels, and loops, validated through experiments with datasets like BPI Challenges.
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.