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 th...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.展开更多
文摘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.
