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
The approach applied by @cite_3 exploits the relational structure between two problems: (i) extracting speech acts, (ii) finding related emails. Instead of attacking them separately, in their synergistic iterative approach, relations identification is used to assist semantic analysis, and vice versa.
@cite_3
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Today’s email clients were designed for yesterday’s email. Originally, email was merely a communication medium. Today, people engage in a variety of complex behaviours using email, such as project management, collaboration, meeting scheduling, to-do tracking, etc. Our goal is to develop automated techniques to help people manage complex activities or tasks in email. The central challenge is that most activities are distributed over multiple messages, yet email clients allow users to manipulate just isolated messages. We describe machine learning approaches to identifying tasks and relations between individual messages in a task (i.e., finding cause-response links between emails) and for semantic message analysis (i.e., extracting metadata about how messages within a task relate to the task progress). Our key innovation compared to related work is that we exploit the relational structure of these two problems. Instead of attacking them separately, in our synergistic iterative approach, relations identification is used to assist semantic analysis, and vice versa. Our experiments with real-world email corpora demonstrate an improvement compared to nonrelational benchmarks.