Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/45535
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMoreira, Catarina-
dc.contributor.authorHaven, Emmanuel-
dc.contributor.authorSozzo, Sandro-
dc.contributor.authorWichert, Andreas-
dc.date.accessioned2019-09-10T13:07:54Z-
dc.date.available2019-09-10T13:07:54Z-
dc.date.issued2018-12-31-
dc.identifier.citationPLoS ONE, 2018, 13(12): e0207806en
dc.identifier.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207806en
dc.identifier.urihttp://hdl.handle.net/2381/45535-
dc.descriptionAll relevant data will be available in the public repository: https://github.com/catarina-moreira/bpmn.en
dc.description.abstractIn this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The event log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service. To this end we study the impact of incomplete event logs (for instance workers forget to register their tasks). The absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results. We use non-classical probability to show we can better reduce the error percentage of inferences as opposed to classical probability.en
dc.description.sponsorshipThis work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013 (https://www.fct.pt/apoios/projectos/consulta/vglobal_projecto.phtml.en?idProjecto=147282&idElemConcurso=8957).en
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/30596655-
dc.rightsCopyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.titleProcess mining with real world financial loan applications: Improving inference on incomplete event logs.en
dc.typeJournal Articleen
dc.identifier.doi10.1371/journal.pone.0207806-
dc.identifier.eissn1932-6203-
dc.identifier.piiPONE-D-18-14153-
dc.description.statusPeer-revieweden
dc.description.versionPublisher Versionen
dc.type.subtypeJournal Article-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIESen
pubs.organisational-group/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Businessen
dc.dateaccepted2018-10-06-
Appears in Collections:Published Articles, School of Management

Files in This Item:
File Description SizeFormat 
journal.pone.0207806.pdfPublished (publisher PDF)7.48 MBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.