Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/22602
Author(s): | Costa, J. Pereira, R. Ribeiro, R. |
Editor: | Soliman, K. S. |
Date: | 2019 |
Title: | ITSM automation - Using machine learning to predict incident resolution category |
Pages: | 5819 - 5830 |
Event title: | 33rd International Business Information Management Association Conference: Education Excellence and Innovation Management through Vision 2020, IBIMA 2019 |
ISBN: | 978-099985512-6 |
Keywords: | ITSM Incident management Natural language Machine learning |
Abstract: | Problem resolution is a key issue in the IT service industry, and it is still difficult for large enterprises to guarantee the service quality of the Incident Management (IM) process because of the difficulty in handling frequent incidents timely, even though IT Service Management (ITSM) standard process have already been established (Zhao & Yang, 2013). In this work, we propose an approach to predict the incident solution category, by exploring and combining the application of natural language processing techniques and machine learning algorithms on a real dataset from a large organization. The tickets contain information across a vast range of subjects from inside the organization with a vocabulary specific to these subjects. By exploring the text-based attributes, our findings show that the full description of an incident is better than the short description and after stop words removal, the use of additional preprocessing techniques and the addition of tickets nominal attributes such as have no impact to the classification performance. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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conferenceobject_61871.pdf | Versão Aceite | 521,83 kB | Adobe PDF | View/Open |
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