Incident and Problem Ticket Clustering and Classification Using Deep Learning  

作　　者：FENG Hailin HAN Jing HUANG Leijun SHENG Ziwei GONG Zican 

机构地区：[1]Zhejiang A&F University,Hangzhou 310007,China [2]ZTE Corporation,Shenzhen 518057,China [3]Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《ZTE Communications》2023年第4期69-77,共9页中兴通讯技术（英文版）

摘　　要：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 Kmeans 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.

关 键 词：problem ticket ticket clustering ticket classification 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]