Multi-level clustering for extracting process-related information from email logs
Jlailaty, Diana; Grigori, Daniela; Belhajjame, Khalid (2017), Multi-level clustering for extracting process-related information from email logs, in Saïd Assar, Oscal Pastor, Haralambos Mouratidis, 11th IEEE International Conference on Research Challenges in Information Science (RCIS 2017), Institute of Electrical and Electronics Engineers, p. 455-456. 10.1109/RCIS.2017.7956583
TypeCommunication / Conférence
Book title11th IEEE International Conference on Research Challenges in Information Science (RCIS 2017)
Book authorSaïd Assar, Oscal Pastor, Haralambos Mouratidis
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Abstract (EN)Emails represent a valuable source of information that can be harvested for understanding undocumented business processes of institutions. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs to make benefit of the many available process mining techniques. In this work, we go further in this direction, by proposing a new method for mining process models from email logs that leverages unsupervised machine learning techniques. Moreover, our method allows to label emails with activity names, that can be used for activity recognition in new incoming emails. A use case illustrates the usefulness of the proposed solution.
Subjects / KeywordsEmail Analysis; Process Model; Process Mining; Process Information; Clustering
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