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

Abstract

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.