<p>Communication
has never been more accessible than today. With the help of Instant messengers
and Email Services, millions of people can transfer information with ease, and
this trend has affected organizations as well. There are billions of
organizational emails sent or received daily, and their main goal is to
facilitate the daily operation of organizations. Behind this vast corpus of
human-generated content, there is much implicit information that can be mined
and used to improve or optimize the organizations’ operations. Business
processes are one of those implicit knowledge areas that can be discovered from
Email logs of an Organization, as most of the communications are followed
inside Emails. The purpose of this research is to propose an approach to
discover the process models in the Email log. In this approach, we combine two
tools, supervised machine learning and process mining. With the help of supervised
machine learning, fastText classifier, we classify the body text of emails to
the activity-related. Then the generated log will be mined with process mining techniques
to find process models. We illustrate the approach with a case study company
from the oil and gas sector.</p>
Machine Learning and Process Mining applied to Process Optimization: Bibliometric and Systemic Analysis
