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dc.contributor.authorJlailaty, Diana
dc.contributor.authorGrigori, Daniela
dc.contributor.authorBelhajjame, Khalid
dc.date.accessioned2019-05-14T09:59:43Z
dc.date.available2019-05-14T09:59:43Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18905
dc.language.isoenen
dc.subjectemailsen
dc.subject.ddc004en
dc.titleA framework for mining process models from emails logsen
dc.typeDocument de travail / Working paper
dc.description.abstractenDue to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs in order to take benefit of the many available process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different process modelsen
dc.publisher.namePreprint Lamsadeen
dc.publisher.cityParisen
dc.identifier.citationpages18en
dc.relation.ispartofseriestitlePreprint Lamsadeen
dc.identifier.urlsitehttps://arxiv.org/abs/1609.06127v1en
dc.subject.ddclabelInformatique généraleen
dc.identifier.citationdate2016-09
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-03-27T15:53:08Z
hal.person.labIds989
hal.person.labIds989
hal.person.labIds989
hal.identifierhal-02128474*


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