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dc.contributor.authorAugusto, A
dc.contributor.authorDumas, M
dc.contributor.authorLa Rosa, M
dc.date.accessioned2021-02-01T01:13:41Z
dc.date.available2021-02-01T01:13:41Z
dc.date.issued2021-01-01
dc.identifier.citationAugusto, A., Dumas, M. & La Rosa, M. (2021). Automated Discovery of Process Models with True Concurrency and Inclusive Choices. Lecture Notes in Business Information Processing, 406 LNBIP, pp.43-56. Springer International Publishing. https://doi.org/10.1007/978-3-030-72693-5_4.
dc.identifier.isbn9783030726928
dc.identifier.issn1865-1348
dc.identifier.urihttp://hdl.handle.net/11343/258885
dc.description.abstractEnterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log – an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.
dc.publisherSpringer International Publishing
dc.source2nd International Conference on Process Mining
dc.titleAutomated Discovery of Process Models with True Concurrency and Inclusive Choices
dc.typeConference Paper
dc.identifier.doi10.1007/978-3-030-72693-5_4
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleLecture Notes in Business Information Processing
melbourne.source.volume406 LNBIP
melbourne.source.pages43-56
melbourne.identifier.arcDP180102839
melbourne.elementsid1493057
melbourne.contributor.authorAugusto, Adriano
melbourne.contributor.authorLa Rosa, Marcello
dc.identifier.eissn1865-1356
melbourne.identifier.fundernameidAustralian Research Council, DP180102839
melbourne.accessrightsOpen Access


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