Business Process Instances Discovery from Email Logs
Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017), Business Process Instances Discovery from Email Logs, 2017 IEEE International Conference on Services Computing (SCC), IEEE - Institute of Electrical and Electronics Engineers : Piscataway, NJ, p. 19-26. 10.1109/SCC.2017.12
TypeCommunication / Conférence
Book title2017 IEEE International Conference on Services Computing (SCC)
MetadataShow full item record
Abstract (EN)Email 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.
Subjects / KeywordsEmail analysis; Word2vec; process instance discovery; process mining; process analysis
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