AI Bibliography
AI Bibliography |
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van Zelst, S. J., Mannhardt, F., de Leoni, M., & Koschmider, A. (2021). Event abstraction in process mining: Literature review and taxonomy. Granular Computing, 6(3), 719–736. |
| Resource type: Journal Article BibTeX citation key: vanZelst2021 View all bibliographic details  |
Categories: Artificial Intelligence, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Mathematics, Military Science Subcategories: Analytics, Behavioral analytics, Big data, Cognitive Electronic Warfare, Command and control, Cyber, Decision making, Deep learning, Forecasting, Informatics, Machine learning, Networked forces, Neural nets, Systems theory Creators: de Leoni, Koschmider, Mannhardt, van Zelst Publisher: Collection: Granular Computing |
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The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.
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