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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Lu Wang, Yuyue Du and Liang Qi, "Efficient Deviation Detection Between a Process Model and Event Logs," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1352-1364, Nov. 2019. doi: 10.1109/JAS.2019.1911750
Citation: Lu Wang, Yuyue Du and Liang Qi, "Efficient Deviation Detection Between a Process Model and Event Logs," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1352-1364, Nov. 2019. doi: 10.1109/JAS.2019.1911750

Efficient Deviation Detection Between a Process Model and Event Logs

doi: 10.1109/JAS.2019.1911750
Funds:  This work was supported by the National Natural Science Foundation of China (61170078, 61472228, 61903229, 61902222), the “Taishan Scholar” Construction Project of Shandong Province, China, the Natural Science Foundation of Shandong Province (ZR2018MF001), the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2017RCJJ044), and the Key Research and Development Program of Shandong Province (2018GGX101011)
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  • Business processes described by formal or semi-formal models are realized via information systems. Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade. By comparing an existing process model with event logs, we can detect inconsistencies called deviations, verify and extend the business process model, and accordingly improve the business process. In this paper, some abnormal activities in business processes are formally defined based on Petri nets. An efficient approach to detect deviations between the process model and event logs is proposed. Then, business process models are revised when abnormal activities exist. A clinical process in a healthcare information system is used as a case study to illustrate our work. Experimental results show the effectiveness and efficiency of the proposed approach.

     

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    Highlights

    • By comparing an existing process model with event logs, we can detect inconsistencies called deviations, verify and extend the business process model, and accordingly improve the business process. In this paper, some abnormal activities in business processes are formally defined based on Petri nets.
    • Given a block-structured process model and a trace that does not conform to the process model, their deviations, can be obtained in a formal way. An efficient approach to detect deviations between them is proposed. The approach can find all the abnormal activities, namely missing, additional and dislocated activities, in traces.
    • When the deviations between a process model and an event log are obtained, we can repair the process model accordingly. The missing activities should be removed from the process model; the additional ones should be inserted into the process model; and for the dislocated ones, the structure of the process model should be changed.

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