A repairing missing activities approach with succession relation for event logs
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A repairing missing activities approach with succession relation for event logs Jie Liu1 · Jiuyun Xu1
· Ruru Zhang2 · Stephan Reiff-Marganiec3
Received: 16 December 2019 / Revised: 17 October 2020 / Accepted: 18 October 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities. Keywords Process mining · Information system · Activity relation matrix · Incomplete event logs
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Jiuyun Xu [email protected] Jie Liu [email protected] Ruru Zhang [email protected] Stephan Reiff-Marganiec [email protected]
1
College of Computer Science and Technology, China University of Petroleum (East China), Tsingdao, China
2
The China Mobile (Suzhou) Software Technology Company, Suzhou, China
3
School of Electronics, Computing and Maths, University of Derby, Derby, UK
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J. Liu et al.
1 Introduction 1.1 Background Business Process Management (BPM) plays an increasingly important role in today’s enterprises because of its advantages of cost savings, improvement of work quality and process optimization. The existing methods and tools in BPM can support the discovery, analysis and improvement of business process [31]. The execution of a business will be documented by the enterprise and eventually will be converted into event log format that contains some attributes such as case id, activity, timestamp and resource. Event logs recorded in modern information system, as the starting point of process mining, provide the precondition for process model discovery, conformance check and enhancement [3]. On the one hand, process discovery in the process mining domain is the crucial learning task whose goal is to automatically discover a process model represented by Petri nets or BPMN in order to analyze actual business execution process [1]. Based on the analysis of event logs, more and more scholars are working on different methods to obtain
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