Partial order resolution of event logs for process conformance checking


Aa, Han van der ; Leopold, Henrik ; Weidlich, Matthias



DOI: https://doi.org/10.1016/j.dss.2020.113347
URL: https://www.sciencedirect.com/science/article/abs/...
Weitere URL: https://arxiv.org/pdf/2007.02416.pdf
Dokumenttyp: Zeitschriftenartikel
Erscheinungsjahr: 2020
Titel einer Zeitschrift oder einer Reihe: Decision Support Systems : DSS
Band/Volume: 136
Heft/Issue: Article 113347
Seitenbereich: 1-34
Ort der Veröffentlichung: Amsterdam [u.a.]
Verlag: Elsevier
ISSN: 0167-9236 , 1873-5797
Sprache der Veröffentlichung: Englisch
Einrichtung: Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Artificial Intelligence Methods (Juniorprofessur) (van der Aa 2020-)
Fachgebiet: 004 Informatik
Abstract: 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 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.




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