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 pro-cess 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 sup-port the discovery of MBPMs from event logs with sub-process multi-instantiation information.In addition,we propose to mea-sure 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 manage-ment 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 pro-cess models with multi-instance sub-processes.

Cong Liu

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

自动化学报（英文版）

[1]Cong Liu-.Formal Modeling and Discovery of Multi-instance Business Processes:A Cloud Resource Management Case Study)[J].自动化学报（英文版）,2022(12):2151-2160

MBPM,instantiations,MBPMs,instantiation

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