Stage-based discovery of business process models from event logs. (September 2019)

• Record Type: Journal Article
• Authors: Nguyen, Hoang; Dumas, Marlon; ter Hofstede, Arthur H.M.; La Rosa, Marcello; Maggi, Fabrizio Maria
• Is Part Of: Information systems. Volume 84(2019)
• Page Start: 214
• Page End: 237
• Publication Date: 2019-09
• Subjects: Process mining -- Automated process discovery -- Modularity; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2019.05.002
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in, and implied by, the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out, however, that existing divide-and-conquer process discovery approaches often produce less accurate models than flat discovery techniques, when applied to real-life event logs. This article proposes an automated method to identify business process stages from an event log and an automated technique to discover process models based on a given stage-based process decomposition. An experimental evaluation shows that: (i) relative to existing automated process decomposition methods in the field of process mining, the proposed method leads to stage-based decompositions that are closer to decompositions derived by human experts; and (ii) the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and achieves comparable results in terms of model complexity. Highlights: We contribute a modularity-based technique to identify stages from an event log. We contribute a technique to discover process models based on stage decompositions. We evaluate model accuracy and complexity using public real-life event logs. The stage identification technique finds stages close to those identified by humans. The discovery technique outperforms existing flat and divide-and-conquer baselines.