Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Discovering Business Processes from Email Logs Using fastText and Process Mining

Version 1 : Received: 1 May 2020 / Approved: 2 May 2020 / Online: 2 May 2020 (12:19:14 CEST)
Version 2 : Received: 2 September 2021 / Approved: 3 September 2021 / Online: 3 September 2021 (10:25:25 CEST)

How to cite: Rashnavadi, Y.; Behzadifard, S.; Farzadnia, R.; Zamani, S. Discovering Business Processes from Email Logs Using fastText and Process Mining. Preprints 2020, 2020050007 (doi: 10.20944/preprints202005.0007.v2). Rashnavadi, Y.; Behzadifard, S.; Farzadnia, R.; Zamani, S. Discovering Business Processes from Email Logs Using fastText and Process Mining. Preprints 2020, 2020050007 (doi: 10.20944/preprints202005.0007.v2).

Abstract

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

Keywords

Process Mining; Business Processes; Natural Language Processing; Machine Learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (1)

Comment 1
Received: 3 September 2021
Commenter: Sina Behzadifard
Commenter's Conflict of Interests: Author
Comment: The changes are as follows: 
1. the text is updated, and the authors added more details about the entire research
2. comments received in the process of peer-review were considered
3. keywords adjusted to elaborate more about the project
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