A framework for detecting deviations in complex event logs

Article type: Research Article

Authors: Li, Guangming^* | van der Aalst, Wil M.P.

Affiliations: Eindhoven University of Technology, Eindhoven, The Netherlands

Correspondence: [*] Corresponding author: Guangming Li, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands. E-mail: [email protected].

Abstract: Deviating behavior within an organization can lead to unexpected results. The effects of deviations are often negative, but sometimes also positive. Therefore, it is useful to detect deviations from event logs which record all the behavior of the organization. However, existing model-based and cluster-based approaches are inaccurate or slow when dealing with complex event logs, i.e. logs of less structured processes having many activities and many possible paths. This paper proposes a novel approach that is faster than cluster-based approaches because it creates a so-called profile which is less time-consuming than creating clusters. Furthermore, the approach is also more accurate than model-based approaches because we use an iterative approach to improve the result. Our experiments show that approach outperforms existing techniques in a variety of circumstances.

Keywords: Process mining, deviation detection, clustering, behavioral profiles

DOI: 10.3233/IDA-160044

Journal: Intelligent Data Analysis, vol. 21, no. 4, pp. 759-779, 2017