Title: Time and structural anomalies detection in business processes using process mining

Authors: Elham Saeedi; Faramarz Safi Esfahani

Addresses: Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran ' Faculty of Computer Engineering; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract: Information systems are increasingly being integrated into operational process and as a result, many events are recorded by information systems. Lack of compatibility between the process model and the observed behaviour is one of the challenges in constructing the process model in process mining. This lack of compatibility could be present in both the structure (sequence of the task) and the time spent in each task. In this paper, a hybrid approach for detecting structural and time anomalies via process mining is proposed. A dataset form Iran Insurance Company is used for performing a case study. The proposed method has detected 98.5% of structure anomalies and 96.3% of time anomalies which is one of the main achievements of this paper. A second standard dataset is used to further examine the proposed method that referred to as dataset 2. The proposed method has demonstrated a better performance compared with the baseline approach.

Keywords: process mining; conformance checking; workflow mining; structural anomaly; time anomaly; flexible model; insurance anomaly; anomaly detection; process model; control-flow perspective.

DOI: 10.1504/IJBIDM.2021.114472

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.3, pp.309 - 331

Received: 16 Aug 2017
Accepted: 21 Aug 2018

Published online: 26 Feb 2021 *

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