Configurable Batch-Processing Discovery from Event Logs

, , & (2022) Configurable Batch-Processing Discovery from Event Logs. ACM Transactions on Management Information Systems, 13(3), Article number: 28.

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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:

7 citations in Scopus
4 citations in Web of Science®
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ID Code: 213735
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
ORCID iD:
Pika, Anastasiiaorcid.org/0000-0001-6452-6915
Ouyang, Chunorcid.org/0000-0001-7098-5480
ter Hofstede, Arthurorcid.org/0000-0002-2730-0201
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
Funding:
Copyright Owner: 2022 Association for Computing Machinery
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Deposited On: 07 Oct 2021 04:08
Last Modified: 04 Aug 2025 04:17