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hal.structure.identifier
dc.contributor.authorJlailaty, Diana*
hal.structure.identifier
dc.contributor.authorGrigori, Daniela*
hal.structure.identifier
dc.contributor.authorBelhajjame, Khalid*
dc.date.accessioned2019-07-23T10:26:19Z
dc.date.available2019-07-23T10:26:19Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19343
dc.description25–30 June 2017Honolulu, Hawaii, USA
dc.language.isoenen
dc.subjectEmail analysis
dc.subjectWord2vec
dc.subjectprocess instance discovery
dc.subjectprocess mining
dc.subjectprocess analysis
dc.subject.ddc004en
dc.titleBusiness Process Instances Discovery from Email Logs
dc.typeCommunication / Conférence
dc.description.abstractenEmail is a reliable, confidential, fast, free and easily accessible form of communication. Due 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. Few researchers have investigated the problem of extracting and analyzing the process-oriented information contained in emails. In this paper, we go forward in this direction by proposing a new method to discover business process instances from email logs that uses unsupervised classification techniques. The approach is composed of two clustering steps. The first one uses a powerful semantic similarity measurement method, Word2vec, while the second one uses a similarity measure combing several email attributes. Experimental results are detailed to illustrate and prove our approach contributions.
dc.identifier.citationpages19-26
dc.relation.ispartoftitle2017 IEEE International Conference on Services Computing (SCC)
dc.relation.ispartofpublnameIEEE - Institute of Electrical and Electronics Engineers
dc.relation.ispartofpublcityPiscataway, NJ
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-1-5386-2004-5
dc.relation.confdate2017
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/SCC.2017.12
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-04-03T07:57:40Z
hal.identifierhal-02191129*
hal.version1*
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hal.update.actionupdateMetadata*
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