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. Towards this aim, a few researchers investigated the problem of extracting process oriented information from email logs in order to take benefit of the many available process mining techniques and tools. In this paper we go further in this direction, by proposing a new method for mining process models from email logs that leverage unsupervised machine learning techniques with little human involvement. Moreover, our method allows to semi-automatically label emails with activity names, that can be used for activity recognition in new incoming emails. A use case demonstrates the usefulness of the proposed solution using a modest in size, yet real-world, dataset containing emails that belong to two different process models
Another category of works deal with the problem of conversation detection in email systems (see for exemple @cite_6 ). While conversation detection is closed to the problem of process instance discovery, they are also different: a conversation is defined as taking place among the same group of people, while a process instance involves different persons, each one having a limited view on the overall set of exchanged emails (i.e., the travel agent only books the tickets, is not implied in the other exchanges).
@cite_6
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This work explores a novel approach for conversation detection in email mailboxes. This approach clusters messages into coherent conversations by using a similarity function among messages that takes into consideration all relevant email attributes, such as message subject, participants, date of submission, and message content. The detection algorithm is evaluated against a manual partition of two email mailboxes into conversations. Experimental results demonstrate the superiority of our detection algorithm over several other alternative approaches.