dc.creator | Valencia Parra, Álvaro | es |
dc.creator | Varela Vaca, Ángel Jesús | es |
dc.creator | Gómez López, María Teresa | es |
dc.creator | Carmona, Josep | es |
dc.creator | Bergenthum, Robin | es |
dc.date.accessioned | 2022-07-18T11:28:51Z | |
dc.date.available | 2022-07-18T11:28:51Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Valencia Parra, Á., Varela Vaca, Á.J., Gómez López, M.T., Carmona, J. y Bergenthum, R. (2021). Empowering conformance checking using Big Data through horizontal decomposition. Information Systems, 99 (July 2021, art. nº 101731) | |
dc.identifier.issn | 0306-4379 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135487 | |
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. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33 | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-86727-C2-1-R | es |
dc.format | application/pdf | es |
dc.format.extent | 17 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Information Systems, 99 (July 2021, art. nº 101731) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Conformance Checking | es |
dc.subject | Decompositional techniques | es |
dc.subject | Big Data | es |
dc.subject | MapReduce | es |
dc.title | Empowering conformance checking using Big Data through horizontal decomposition | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-094283-B-C33 | es |
dc.relation.projectID | TIN2017-86727-C2-1-R | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0306437921000077 | es |
dc.identifier.doi | 10.1016/j.is.2021.101731 | es |
dc.contributor.group | Universidad de Sevilla. TIC258: Data-centric Computing Research Hub | es |
dc.journaltitle | Information Systems | es |
dc.publication.volumen | 99 | es |
dc.publication.issue | July 2021, art. nº 101731 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |