Wubai Zhou (Florida International University);Wei Xue (Florida International University);Tao Li (Florida International University);Chunqiu Zeng (Florida International University);Wang Qing (Florida International University);Larisa Shwartz (IBM Research);Genady Ya. Grabarnik (St. John's University)
In large scale and complex IT service environments, a problematic incident is logged as a ticket which contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring the similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets along with their quality scores obtained from the resolution quality quantification are then used to train a deep neural network ranking model which outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.