Alignment-based conformance checking over probabilistic events

dc.contributor.author Zheng, Jiawei
dc.contributor.author Papapanagiotou, Petros
dc.contributor.author Fleuriot, Jacques
dc.date.accessioned 2023-12-26T18:48:29Z
dc.date.available 2023-12-26T18:48:29Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other bbaba54a-8dfd-4183-9f93-0b35f95eea0c
dc.identifier.uri https://hdl.handle.net/10125/107104
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Business Process Technology
dc.subject conformance checking
dc.subject probabilistic cost function
dc.subject probabilistic events
dc.subject uncertainty
dc.title Alignment-based conformance checking over probabilistic events
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Conformance 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.extent 10 pages
prism.startingpage 5982
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