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ZTE Communications ›› 2023, Vol. 21 ›› Issue (4): 69-77.DOI: 10.12142/ZTECOM.202304009

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  • 收稿日期:2022-12-19 出版日期:2023-12-07 发布日期:2023-12-07

Incident and Problem Ticket Clustering and Classification Using Deep Learning

FENG Hailin1, HAN Jing2(), HUANG Leijun1, SHENG Ziwei3, GONG Zican2   

  1. 1.Zhejiang A&F University, Hangzhou 310007, China
    2.ZTE Corporation, Shenzhen 518057, China
    3.Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2022-12-19 Online:2023-12-07 Published:2023-12-07
  • About author:FENG Hailin received his PhD in computer science from the University of Science and Technology of China in June 2007. Since 2007, he has been working in the School of Information Engineering of Zhejiang A&F University, China. From 2013 to 2014, he was a visiting professor at Forest Products Laboratory, USA. He is currently a professor in the School of Mathematics and Computer Science and School of Information Engineering of Zhejiang A&F University. His main interest areas include computer vision, intelligent information processing, and Internet of Things.
    HAN Jing (han.jing28@zte.com.cn) received her master’s degree from Nanjing University of Aeronautics and Astronautics, China. She has been with ZTE Corporation since 2000, where she worked on 3G/4G key technologies from 2000 to 2016 and has become a technical director responsible for intelligent operation of cloud platforms and wireless networks since 2016. Her research interests include machine learning, data mining, and signal processing.
    HUANG Leijun received his PhD in computer science from George Mason University, USA in 2008. Since 2010, he has been working in the School of Information Engineering of Zhejiang A&F University, China. He is currently a lecturer in the School of Mathematics and Computer Science. His main interest areas include computer networks, Internet of Things and data mining.
    SHENG Ziwei received her BS degree in software engineering from Huazhong University of Science and Technology, China in 2022. She is currently pursuing her MS degree in electrical and computer engineering at Carnegie Mellon University, USA. In her master’s program, she primarily focuses on the fields of engineering development and system design. Her ultimate goal is to advance technology and foster innovation in these domains.
    GONG Zican received his master’s degree in professional computing and artificial intelligence from the Australian National University in 2019. He has been a machine learning engineer in ZTE Corporation since 2020. His research interests include machine learning, professional computing and system architecture.

Abstract:

A holistic analysis of problem and incident tickets in a real production cloud service environment is presented in this paper. By extracting different bags of words, we use principal component analysis (PCA) to examine the clustering characteristics of these tickets. Then K-means and latent Dirichlet allocation (LDA) are applied to show the potential clusters within this Cloud environment. The second part of our study uses a pre-trained bidirectional encoder representation from transformers (BERT) model to classify the tickets, with the goal of predicting the optimal dispatching department for a given ticket. Experimental results show that due to the unique characteristics of ticket description, pre-processing with domain knowledge turns out to be critical in both clustering and classification. Our classification model yields 86% accuracy when predicting the target dispatching department.

Key words: problem ticket, ticket clustering, ticket classification