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Predicting Order Activity Sequence Using Contextual Process Mining

2026-03-24Comunicação em conferência Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
dc.contributor.authorBarbeiro, Diogo A.
dc.contributor.authorMartinho, Ricardo F. G.
dc.contributor.authorFerreira, Carlos J. R.
dc.date.accessioned2026-04-28T11:37:32Z
dc.date.available2026-04-28T11:37:32Z
dc.date.issued2026-03-24
dc.date.updated2026-04-23T12:07:32Z
dc.descriptionCENTERIS - International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2025; 26 a 28 de novembro de 2025; Abu Dhabi, Emirados Árabes Unidos.
dc.description.abstractLogistics processes depend heavily on changing conditions and making accurate forecasts is becoming more and more important to preventing delays and/or predict risks. Predictive Process Monitoring has advanced through deep learning and process-mining approaches, yet current methods often lack interpretability and lose accuracy when context varies. Recent research shows that contextual factors can improve predictions, but their integration into transparent, model-driven frameworks remains limited. This article presents a context-aware predictive approach that filters historical event logs by important attributes, discovers process models with the Inductive Miner, and predicts future activities and timestamps using token-based replay and polynomial regression. Experiments with real logistics data show that incorporating context reduces prediction errors, while the use of process mining ensures an interpretable and operationally practical forecasting solution for logistics environments.eng
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.citationBarbeiro, D. A., Martinho, R. F. G., & Ferreira, C. J. R. (2026). Predicting Order Activity Sequence Using Contextual Process Mining. Procedia Computer Science, 278, 898–905. https://doi.org/10.1016/j.procs.2026.03.064
dc.identifier.doi10.1016/j.procs.2026.03.064en_US
dc.identifier.issn1877-0509en_US
dc.identifier.slugcv-prod-5021476
dc.identifier.urihttp://hdl.handle.net/10400.8/16210
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1877050926006563
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectProcess Mining
dc.subjectPredictive Monitoring
dc.subjectSpatio-temporal Analysis
dc.subjectUsability Testing
dc.subjectDecision Support Systems
dc.titlePredicting Order Activity Sequence Using Contextual Process Miningeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2025-11
oaire.citation.conferencePlaceAbu Dhabi, Emirados Árabes Unidos
oaire.citation.endPage905
oaire.citation.startPage898
oaire.citation.titleProcedia Computer Scienceen_US
oaire.citation.volume278en_US
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMartinho
person.givenNameRicardo
person.identifier.ciencia-idF51E-9BB5-EF92
person.identifier.orcid0000-0003-1157-7510
person.identifier.ridK-8277-2013
person.identifier.scopus-author-id25823103700
rcaap.cv.cienciaidF51E-9BB5-EF92 | Ricardo Martinho
rcaap.rightsopenAccessen_US
relation.isAuthorOfPublicationb2a74e46-f06c-4dcd-8c64-8f78f1d55440
relation.isAuthorOfPublication.latestForDiscoveryb2a74e46-f06c-4dcd-8c64-8f78f1d55440

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