Configurable Batch-Processing Discovery from Event Logs
| Autor: | Anastasiia Pika, Chun Ouyang, Arthur H. M. ter Hofstede |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ACM Transactions on Management Information Systems. 13:1-25 |
| ISSN: | 2158-6578 2158-656X |
| DOI: | 10.1145/3490394 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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