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dc.contributor.authorDurán-Muñoz, Francisco Javier 
dc.contributor.authorPozas, Nicolás
dc.contributor.authorRocha, Camilo
dc.date.accessioned2024-11-26T12:16:53Z
dc.date.available2024-11-26T12:16:53Z
dc.date.issued2024
dc.identifier.citationFrancisco Durán, Nicolás Pozas, Camilo Rocha: Business processes resource management using rewriting logic and deep-learning-based predictive monitoring. J. Log. Algebraic Methods Program. 136: 100928 (2024)es_ES
dc.identifier.urihttps://hdl.handle.net/10630/35327
dc.description.abstractA significant task in business process optimization is concerned with streamlining the allocation and sharing of resources. This paper presents an approach for analyzing business process provisioning under a resource prediction strategy based on deep learning. A timed and probabilistic rewrite theory specification formalizes the semantics of business processes. It is integrated with an external oracle in the form of a long short-term memory neural network that can be queried to predict how traces of the process may advance within a time frame. Comparison of execution time and resource occupancy under different parameters is included for several case studies, as well as details on the construction of the deep learning model and its integration with Maude.es_ES
dc.description.sponsorshipThe first two authors have been partially supported by projects TED2021-130666B-I00 and PID2021-125527NB-I00 funded by the Spanish government. The work of Rocha was partially funded by the Minciencias (Ministerio de Ciencia Tecnología e Innovación, Colombia) project PROMUEVA (BPIN 2021000100160).es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectLogística empresariales_ES
dc.subjectPlanificación estratégicaes_ES
dc.subject.otherMaudees_ES
dc.subject.otherBusiness processes_ES
dc.subject.otherPredictive monitoringes_ES
dc.titleBusiness processes resource management using rewriting logic and deep-learning-based predictive monitoringes_ES
dc.typejournal articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doi10.1016/j.jlamp.2023.100928
dc.type.hasVersionSMURes_ES
dc.departamentoInstituto de Tecnología e Ingeniería del Software de la Universidad de Málaga
dc.rights.accessRightsopen accesses_ES


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