Slice, mine and dice : complexity-aware automated discovery of business process models

Ekanayake, Chathura C., Dumas, Marlon, Garcia-Banuelos, Luciano, & La Rosa, Marcello (2013) Slice, mine and dice : complexity-aware automated discovery of business process models. In 11th Int. Conference on Business Process Management .

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Automated process discovery techniques aim at extracting models from information system logs in order to shed light into the business processes supported by these systems.

Existing techniques in this space are effective when applied to relatively small or regular logs, but otherwise generate large and spaghetti-like models. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. The result is a collection of process models -- each one representing a variant of the business process -- as opposed to an all-encompassing model.

Still, models produced in this way may exhibit unacceptably high complexity. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically by means of subprocess extraction. The proposed technique allows users to set a desired bound for the complexity of the produced models. Experiments on real-life logs show that the technique produces collections of models that are up to 64% smaller than those extracted under the same complexity bounds by applying existing trace clustering techniques.

Impact and interest:

6 citations in Scopus
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ID Code: 58949
Item Type: Conference Paper
Refereed: No
Keywords: business process management, process mining
DOI: 10.1007/978-3-642-40176-3_6
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Past > Schools > Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 (please consult the authors).
Deposited On: 08 Apr 2013 01:11
Last Modified: 22 Aug 2013 21:56

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