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dc.contributor.authorTaymouri, Farbod
dc.contributor.authorLa Rosa, Marcello
dc.contributor.authorCarmona Vargas, Josep
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-10-30T08:38:12Z
dc.date.available2020-10-30T08:38:12Z
dc.date.issued2020
dc.identifier.citationTaymouri, F.; La Rosa, M.; Carmona, J. Business process variant analysis based on mutual fingerprints of event logs. A: International Conference on Advanced Information Systems Engineering. "Advanced Information Systems Engineering, 32nd International Conference, CAiSE 2020: Grenoble, France, June 8–12, 2020: proceedings". Berlín: Springer, 2020, p. 299-318. ISBN 978-3-030-49435-3. DOI 10.1007/978-3-030-49435-3_19.
dc.identifier.isbn978-3-030-49435-3
dc.identifier.urihttp://hdl.handle.net/2117/331005
dc.description.abstractComparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.
dc.description.sponsorshipThis research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R).
dc.format.extent20 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshData mining
dc.subject.lcshGraph theory
dc.titleBusiness process variant analysis based on mutual fingerprints of event logs
dc.typeConference report
dc.subject.lemacMineria de dades
dc.subject.lemacGrafs, Teoria de
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.identifier.doi10.1007/978-3-030-49435-3_19
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-49435-3_19
dc.rights.accessOpen Access
local.identifier.drac29587844
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86727-C2-1-R/ES/MODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALA/
local.citation.authorTaymouri, F.; La Rosa, M.; Carmona, J.
local.citation.contributorInternational Conference on Advanced Information Systems Engineering
local.citation.pubplaceBerlín
local.citation.publicationNameAdvanced Information Systems Engineering, 32nd International Conference, CAiSE 2020: Grenoble, France, June 8–12, 2020: proceedings
local.citation.startingPage299
local.citation.endingPage318


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