Mining Business Process Activities from Email Logs
Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017), Mining Business Process Activities from Email Logs, 2017 IEEE International Conference on Cognitive Computing (ICCC), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 112-119. 10.1109/IEEE.ICCC.2017.28
Type
Communication / ConférenceDate
2017Conference date
2017Book title
2017 IEEE International Conference on Cognitive Computing (ICCC)Publisher
IEEE - Institute of Electrical and Electronics Engineers
Published in
Piscataway, NJ
ISBN
978-1-5386-2007-6
Pages
112-119
Publication identifier
Metadata
Show full item recordAbstract (EN)
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 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.Subjects / Keywords
Email analysis; Word2vec; LSA; process mining; process modelingRelated items
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