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dc.contributor.authorJlailaty, Diana*
dc.contributor.authorGrigori, Daniela*
dc.contributor.authorBelhajjame, Khalid*
dc.date.accessioned2019-07-23T10:31:30Z
dc.date.available2019-07-23T10:31:30Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19344
dc.language.isoenen
dc.subjectEmail Analysis
dc.subjectProcess Model
dc.subjectProcess Mining
dc.subjectProcess Information
dc.subjectClustering
dc.subject.ddc004en
dc.titleMulti-level clustering for extracting process-related information from email logs
dc.typeCommunication / Conférence
dc.description.abstractenEmails represent a valuable source of information that can be harvested for understanding undocumented business processes of institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs to make benefit of the many available process mining techniques. In this work, we go further in this direction, by proposing a new method for mining process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.
dc.identifier.citationpages455-456
dc.relation.ispartoftitle11th IEEE International Conference on Research Challenges in Information Science (RCIS 2017)
dc.relation.ispartofeditorSaïd Assar, Oscal Pastor, Haralambos Mouratidis
dc.relation.ispartofpublnameInstitute of Electrical and Electronics Engineers
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-1-5090-5476-3
dc.relation.confdate2017
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/RCIS.2017.7956583
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-04-03T07:58:15Z
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*
hal.identifierhal-02191133*


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