Partial order resolution of event logs for process conformance checking
- While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based onWhile supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.…
Author details: | Han van der AaORCiDGND, Henrik LeopoldORCiDGND, Matthias WeidlichORCiDGND |
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DOI: | https://doi.org/10.1016/j.dss.2020.113347 |
ISSN: | 0167-9236 |
ISSN: | 1873-5797 |
Title of parent work (English): | Decision support systems : DSS |
Publisher: | Elsevier |
Place of publishing: | Amsterdam [u.a.] |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/07/12 |
Publication year: | 2020 |
Release date: | 2023/11/10 |
Tag: | conformance checking; data; partial order resolution; process mining; uncertainty |
Volume: | 136 |
Article number: | 113347 |
Number of pages: | 12 |
Funding institution: | Alexander von Humboldt Foundation |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |