Configurable Batch-Processing Discovery from Event Logs
Pika, Anastasiia, Ouyang, Chun, & ter Hofstede, Arthur (2022) Configurable Batch-Processing Discovery from Event Logs. ACM Transactions on Management Information Systems, 13(3), Article number: 28.
Description
Batch processing is used in many production and service processes and can help achieve efficiencies of scale; however, it can also increase inventories and introduce process delays. Before organizations can develop good understanding about the effects of batch processing on process performance, they should be able to identify potential batch-processing behavior in business processes. However, in many cases such behavior may not be known; for example, batch processing may be occasionally performed during certain time frames, by specific employees, and/or for particular customers. This article presents a novel approach for the identification of batching behavior from process execution data recorded in event logs. The approach can discover different types of batch-processing behaviors and allows users to configure batch-processing characteristics they are interested in. The approach is implemented and evaluated through experiments with synthetic event logs and case studies with real-life event logs. The evaluation demonstrates that the approach can identify various batch-processing behaviors in the context of business processes.
Impact and interest:
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ID Code: | 213735 | ||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||
Refereed: | Yes | ||||||
ORCID iD: |
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Additional Information: | Acknowledgements: The research reported in this paper was supported by the Australian Research Council Discovery Grant DP150103356. | ||||||
Measurements or Duration: | 25 pages | ||||||
Keywords: | batch processing, batch processing discovery, event log, process mining | ||||||
DOI: | 10.1145/3490394 | ||||||
ISSN: | 2158-6578 | ||||||
Pure ID: | 99397648 | ||||||
Divisions: | Current > Research Centres > Centre for Behavioural Economics, Society & Technology Current > Research Centres > Centre for Data Science Current > QUT Faculties and Divisions > Faculty of Business & Law Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Information Systems |
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Copyright Owner: | 2022 Association for Computing Machinery | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to [email protected] | ||||||
Deposited On: | 07 Oct 2021 04:08 | ||||||
Last Modified: | 04 Aug 2025 04:17 |
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