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Multi-level clustering for extracting process-related information from email logs

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Date
2017
Dewey
Informatique générale
Sujet
Email Analysis; Process Model; Process Mining; Process Information; Clustering
DOI
http://dx.doi.org/10.1109/RCIS.2017.7956583
Conference date
2017
Book title
11th IEEE International Conference on Research Challenges in Information Science (RCIS 2017)
Author
Saïd Assar, Oscal Pastor, Haralambos Mouratidis
Publisher
Institute of Electrical and Electronics Engineers
ISBN
978-1-5090-5476-3
URI
https://basepub.dauphine.fr/handle/123456789/19344
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Jlailaty, Diana
Grigori, Daniela
Belhajjame, Khalid
Type
Communication / Conférence
Item number of pages
455-456
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.

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