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Empowering conformance checking using Big Data through horizontal decomposition
dc.contributor.author | Valencia Parra, Álvaro |
dc.contributor.author | Varela Vaca, Ángel Jesús |
dc.contributor.author | Gómez López, María Teresa |
dc.contributor.author | Carmona Vargas, Josep |
dc.contributor.author | Bergenthum, Robin |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2021-05-04T14:29:40Z |
dc.date.available | 2023-02-18T01:26:39Z |
dc.date.issued | 2021-07 |
dc.identifier.citation | Valencia, Á. [et al.]. Empowering conformance checking using Big Data through horizontal decomposition. "Information systems", Juliol 2021, vol. 99, article 101731, p. 1-17. |
dc.identifier.issn | 0306-4379 |
dc.identifier.uri | http://hdl.handle.net/2117/345143 |
dc.description.abstract | Conformance checking unleashes the full power of process mining: techniques from this discipline enable the analysis of the quality of a process model through the discovery of event data, the identification of potential deviations, and the projection of real traces onto process models. In this way, the insights gained from the available event data can be transferred to a richer conceptual level, amenable for human interpretation. Unfortunately, most of the aforementioned functionalities are grounded in an extremely difficult fundamental problem: given an observed trace and a process model, find the model trace that most closely resembles to the trace observed. This paper presents an architecture that supports the creation and distribution of alignment subproblems based on an innovative horizontal acyclic model decomposition, disengaged from the conformance checking algorithm applied for their solution. This is supported by a Big Data infrastructure that facilitates the customised distribution of a gross amount of data. Experiments are provided that testify to the enormous potential of the architecture proposed, thereby opening the door to further research in several directions. |
dc.description.sponsorship | This work has been partially funded by the Ministry of Science and Technology of Spain ECLIPSE (RTI2018-094283-B-C33) project, the European Regional Development Fund (ERDF/FEDER), MINECO (TIN2017-86727-C2-1-R), and by the University of Seville with VI Plan Propio de Investigación y Transferencia (VI PPIT-US). |
dc.format.extent | 17 p. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights | © 2021 Elsevier |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject.lcsh | Big data |
dc.subject.lcsh | Process mining |
dc.subject.lcsh | Data mining |
dc.subject.other | Conformance checking |
dc.subject.other | Decompositional techniques |
dc.subject.other | MapReduce |
dc.title | Empowering conformance checking using Big Data through horizontal decomposition |
dc.type | Article |
dc.subject.lemac | Dades massives |
dc.subject.lemac | Mineria de dades |
dc.contributor.group | Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals |
dc.identifier.doi | 10.1016/j.is.2021.101731 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0306437921000077 |
dc.rights.access | Open Access |
local.identifier.drac | 31251156 |
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 | Valencia, Á.; Varela, Á.; Gómez, M.; Carmona, J.; Bergenthum, R. |
local.citation.publicationName | Information systems |
local.citation.volume | 99 |
local.citation.number | article 101731 |
local.citation.startingPage | 1 |
local.citation.endingPage | 17 |
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