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dc.contributor.authorJlailaty, Diana*
dc.contributor.authorGrigori, Daniela*
dc.contributor.authorBelhajjame, Khalid*
dc.date.accessioned2019-04-19T10:16:10Z
dc.date.available2019-04-19T10:16:10Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18708
dc.descriptionHonolulu, juin 2017
dc.language.isoenen
dc.subjectEmail analysis
dc.subjectWord2vec
dc.subjectLSA
dc.subjectprocess mining
dc.subjectprocess modeling
dc.subject.ddc004en
dc.titleMining Business Process Activities from Email Logs
dc.typeCommunication / Conférence
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. 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 activities from email logs. Towards this aim, emails are grouped according to the process model they belong to. This is followed by sub-grouping and labeling the emails of each process model into business activity types. These tasks are applied by deploying an unsupervised mining technique accompanied by semantic similarity measurement methods. Two representative similarity measurement methods are examined: Latent Semantic Indexing (LSA) and Word2vec. These methods are compared to prove that Word2vec provides a better performance than LSA in grouping emails according to what process model they are related to, and in discovering emails belonging to the same activity type. Experimental results are detailed to illustrate and prove our approach contributions.
dc.identifier.citationpages112-119
dc.relation.ispartoftitle2017 IEEE International Conference on Cognitive Computing (ICCC)
dc.relation.ispartofpublnameIEEE - Institute of Electrical and Electronics Engineers
dc.relation.ispartofpublcityPiscataway, NJ
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-1-5386-2007-6
dc.relation.confdate2017
dc.relation.forthcomingnonen
dc.identifier.doi10.1109/IEEE.ICCC.2017.28
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2020-04-03T07:58:56Z
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*
hal.identifierhal-02104140*


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