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Discovering Process Horizontal Boundaries to Facilitate Process Comprehension

Discovering Process Horizontal Boundaries to Facilitate Process Comprehension

Pavlos Delias, Kleanthi Lakiotaki
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 31
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781522544616|DOI: 10.4018/IJORIS.2018040101
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MLA

Delias, Pavlos, and Kleanthi Lakiotaki. "Discovering Process Horizontal Boundaries to Facilitate Process Comprehension." IJORIS vol.9, no.2 2018: pp.1-31. http://doi.org/10.4018/IJORIS.2018040101

APA

Delias, P. & Lakiotaki, K. (2018). Discovering Process Horizontal Boundaries to Facilitate Process Comprehension. International Journal of Operations Research and Information Systems (IJORIS), 9(2), 1-31. http://doi.org/10.4018/IJORIS.2018040101

Chicago

Delias, Pavlos, and Kleanthi Lakiotaki. "Discovering Process Horizontal Boundaries to Facilitate Process Comprehension," International Journal of Operations Research and Information Systems (IJORIS) 9, no.2: 1-31. http://doi.org/10.4018/IJORIS.2018040101

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Abstract

Automated discovery of a process model is a major task of Process Mining that means to produce a process model from an event log, without any a-priori information. However, when an event log contains a large number of distinct activities, process discovery can be real challenging. The goal of this article is to facilitate process discovery in such cases when a process is expected to contain a large set of unique activities. To this end, this article proposes a clustering approach that recommends horizontal boundaries for the process. The proposed approach ultimately partitions the event log in a way that human interpretation efforts are decomposed. In addition, it makes automated discovery more efficient as well as effective by simultaneously considering two quality criteria: informativeness and robustness of the derived groups of activities. The authors conducted several experiments to test the behavior of the algorithm under different settings, and to compare it against other techniques. Finally, they provide a set of recommendations that may help process analysts during the process discovery endeavor.

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