Mining Frequent Patterns in Process Models

TítuloMining Frequent Patterns in Process Models
AutoresDavid Chapela-Campa, Manuel Mucientes, Manuel Lama
TipoArtículo de revista
Fonte Information Sciences, ELSEVIER SCIENCE INC, Vol. 472, pp. 235-257 , 2019.
RankRanked Q1 in Information Systems and Management by SJR
ISSN0020-0255
DOI10.1016/j.ins.2018.09.011
AbstractProcess 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.
Palabras chaveFrequent pattern mining, Process mining, Process discovery