Alignment-based conformance checking over probabilistic events

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dc.contributor.authorZheng, Jiawei
dc.contributor.authorPapapanagiotou, Petros
dc.contributor.authorFleuriot, Jacques
dc.date.accessioned2023-12-26T18:48:29Z
dc.date.available2023-12-26T18:48:29Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.719
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherbbaba54a-8dfd-4183-9f93-0b35f95eea0c
dc.identifier.urihttps://hdl.handle.net/10125/107104
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBusiness Process Technology
dc.subjectconformance checking
dc.subjectprobabilistic cost function
dc.subjectprobabilistic events
dc.subjectuncertainty
dc.titleAlignment-based conformance checking over probabilistic events
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractConformance checking techniques allow us to evaluate how well some exhibited behaviour, represented by a trace of monitored events, conforms to a specified process model. Modern monitoring and activity recognition technologies, such as those relying on sensors, the IoT, statistics and AI, can produce a wealth of relevant event data. However, this data is typically characterised by noise and uncertainty, in contrast to the assumption of a deterministic event log required by conformance checking algorithms. In this paper, we extend alignment-based conformance checking to function under a probabilistic event log. We introduce a weighted trace model and weighted alignment cost function, and a custom threshold parameter that controls the level of confidence on the event data vs. the process model. The resulting algorithm considers activities of lower but sufficiently high probability that better align with the process model. We explain the algorithm and its motivation both from formal and intuitive perspectives, and demonstrate its functionality in comparison with deterministic alignment using real-life datasets.
dcterms.extent10 pages
prism.startingpage5982

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