Detecting sudden and gradual drifts in business processes from execution traces
| Autor: | Alireza Ostovar, Marcello La Rosa, Marlon Dumas, Abderrahmane Maaradji |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Concept drift Computer science Business process business.industry Computer Science - Artificial Intelligence Real-time computing Process (computing) Process mining 02 engineering and technology Computer Science Applications Business process management Artificial Intelligence (cs.AI) Computational Theory and Mathematics 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Information Systems |
| Popis: | Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early detection of business process changes enables managers to identify and act upon changes that may otherwise affect process performance. Business process drift detection refers to a family of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift. Depending on the explored feature space, these methods miss various types of changes. Moreover, they are either designed to detect sudden drifts or gradual drifts but not both. This paper proposes an automated and statistically grounded method for detecting sudden and gradual business process drifts under a unified framework. An empirical evaluation shows that the method detects typical change patterns with significantly higher accuracy and lower detection delay than existing methods, while accurately distinguishing between sudden and gradual drifts. |
| Databáze: | OpenAIRE |
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