A novel approach for process mining based on event types |
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| Authors: | Lijie Wen Jianmin Wang Wil M P van der Aalst Biqing Huang Jiaguang Sun |
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| Affiliation: | (1) School of Software, Tsinghua University, Beijing, China;(2) Key Laboratory for Information System Security, Ministry of Education, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, 100084, P.R. China;(3) Department of Automation, Tsinghua University, Beijing, China;(4) Department of Technology Management, Eindhoven University of Technology, Eindhoven, The Netherlands |
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| Abstract: | Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic
information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques
and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea
of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges
it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process
mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks
can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing
algorithms such as the α-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.
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| Keywords: | Process mining Workflow mining Data mining Event types Petri nets WF-nets DWF-nets |
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