Alignment-based conformance checking over probabilistic events

Jiawei Zheng, Petros Papapanagiotou, Jacques D Fleuriot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication57th Hawaii International Conference on System Sciences
PublisherUniversity of Hawaiʻi at Mānoa
Number of pages10
ISBN (Electronic)978-0-9981331-7-1
Publication statusPublished - 3 Jan 2024
Event57th Hawaii International Conference on System Sciences - Waikiki, United States
Duration: 3 Jan 20246 Jan 2024
Conference number: 57,%2D%20Hawaiian%20Village%2C%20Waikiki%2C%20HI


Conference57th Hawaii International Conference on System Sciences
Abbreviated titleHICSS 2024
Country/TerritoryUnited States
Internet address


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