Paper Info
Title
Stage-based discovery of business process models from event logs
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.
Year
DOI
Venue
2019
10.1016/j.is.2019.05.002
Information Systems
Keywords
DocType
Volume
Process mining,Automated process discovery,Modularity
Journal
84
ISSN
Citations 
PageRank 
0306-4379
2
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
Hoang Nguyen1272.65
Marlon Dumas25742371.10
Arthur H. M. ter Hofstede353752.24
marcello la rosa4140281.70
Fabrizio Maria Maggi54620.83