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Volume 9 Issue 12
Dec.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(12): 2151-2160
C. Liu, “Formal modeling and discovery of multi-instance business processes: A cloud resource management case study,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2151–2160, Dec. 2022. doi: 10.1109/JAS.2022.106109
Citation: C. Liu, “Formal modeling and discovery of multi-instance business processes: A cloud resource management case study,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2151–2160, Dec. 2022. doi: 10.1109/JAS.2022.106109
shu

Formal Modeling and Discovery of Multi-instance Business Processes: A Cloud Resource Management Case Study

doi: 10.1109/JAS.2022.106109

  • School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, and also with the College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Funds:  This work was supported by the National Natural Science Foundation of China (61902222), the Taishan Scholars Program of Shandong Province (tsqn201909109), the Natural Science Excellent Youth Foundation of Shandong Province (ZR2021YQ45), and the Youth Innovation Science and Technology Team Foundation of Shandong Higher School (2021KJ031)
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    Abstract
  • Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years; however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model (MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets (MPNs) that are an extension of Petri nets with distinguishable tokens. Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used. The proposed discovery approach is properly implemented as plugins in the ProM toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-the-art process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.

     

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