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dc.contributor.authorKalenkova, A
dc.contributor.authorPolyvyanyy, A
dc.date.accessioned2020-11-26T22:22:40Z
dc.date.available2020-11-26T22:22:40Z
dc.date.issued2020
dc.identifier.citationKalenkova, A. & Polyvyanyy, A. (2020). A Spectrum of Entropy-Based Precision and Recall Measurements Between Partially Matching Designed and Observed Processes. ICSOC 2020 proceedings, 12571 LNCS, pp.337-354. Springer. https://doi.org/10.1007/978-3-030-65310-1_24.
dc.identifier.isbn9783030653095
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11343/251953
dc.description.abstractModern software systems are often built using service-oriented principles. Atomic components, be that web-or micro services, allow constructing flexible and loosely coupled systems. In such systems, services are building blocks orchestrated by business processes the system supports. Due to the complexity and heterogeneity of industrial software systems, implemented processes may deviate from those initially designed. In this paper, we propose a spectrum of conformance measurements. The spectrum results from a generalization of the recently introduced entropy-based approaches for measuring precision and recall between observed process executions and designed process models. The new generalized measures of precision and recall inherit the desired for this class of measures properties and provide analysts with flexible control over the sensitivity for identifying commonalities and discrepancies in the compared processes and performance of the techniques. The reported evaluation based on our implementation of the measures over real-world event logs and automatically discovered models confirms the feasibility of using the approach in industrial settings.
dc.publisherSpringer
dc.sourceThe 18th International Conference on Service Oriented Computing (ICSOC 2020)
dc.titleA Spectrum of Entropy-Based Precision and Recall Measurements Between Partially Matching Designed and Observed Processes
dc.typeConference Paper
dc.identifier.doi10.1007/978-3-030-65310-1_24
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleLecture Notes in Artificial Intelligence
melbourne.source.volume12571 LNCS
melbourne.source.pages337-354
melbourne.identifier.arcDP180102839
melbourne.elementsid1480881
melbourne.contributor.authorKalenkova, Anna
melbourne.contributor.authorPolyvyanyy, Artem
dc.identifier.eissn1611-3349
melbourne.identifier.fundernameidAUST RESEARCH COUNCIL, DP180102839
melbourne.event.locationDubai
melbourne.accessrightsOpen Access


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