BPMN miner: Automated discovery of BPMN process models with hierarchical structure

Conforti, Raffaele, Dumas, Marlon, García-Bañuelos, Luciano, & La Rosa, Marcello (2016) BPMN miner: Automated discovery of BPMN process models with hierarchical structure. Information Systems, 56, pp. 284-303.

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Abstract

Existing techniques for automated discovery of process models from event logs generally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models containing interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery techniques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Impact and interest:

3 citations in Scopus
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1 citations in Web of Science®

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ID Code: 83646
Item Type: Journal Article
Refereed: Yes
Keywords: automated process discovery, BPMN, process mining, business process management
DOI: 10.1016/j.is.2015.07.004
ISSN: 0306-4379
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Elsevier
Copyright Statement: Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International. DOI:10.1016/j.is.2015.07.004
Deposited On: 16 Apr 2015 02:47
Last Modified: 02 Apr 2017 18:46

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