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Business process variant analysis based on mutual fingerprints of event logs
dc.contributor.author | Taymouri, Farbod |
dc.contributor.author | La Rosa, Marcello |
dc.contributor.author | Carmona Vargas, Josep |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2020-10-30T08:38:12Z |
dc.date.available | 2020-10-30T08:38:12Z |
dc.date.issued | 2020 |
dc.identifier.citation | Taymouri, 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.isbn | 978-3-030-49435-3 |
dc.identifier.uri | http://hdl.handle.net/2117/331005 |
dc.description.abstract | Comparing 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.sponsorship | This research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R). |
dc.format.extent | 20 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject.lcsh | Data mining |
dc.subject.lcsh | Graph theory |
dc.title | Business process variant analysis based on mutual fingerprints of event logs |
dc.type | Conference report |
dc.subject.lemac | Mineria de dades |
dc.subject.lemac | Grafs, Teoria de |
dc.contributor.group | Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals |
dc.identifier.doi | 10.1007/978-3-030-49435-3_19 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-49435-3_19 |
dc.rights.access | Open Access |
local.identifier.drac | 29587844 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info: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.author | Taymouri, F.; La Rosa, M.; Carmona, J. |
local.citation.contributor | International Conference on Advanced Information Systems Engineering |
local.citation.pubplace | Berlín |
local.citation.publicationName | Advanced Information Systems Engineering, 32nd International Conference, CAiSE 2020: Grenoble, France, June 8–12, 2020: proceedings |
local.citation.startingPage | 299 |
local.citation.endingPage | 318 |