Towards an optimized model of incident ticket correlation
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Service desks are used by customers to report IT issues in enterprise systems. Most of these service requests are resolved by level-1 persons (service desk attendants) by providing information/quick-fix solutions to customers. For each service request, level-1 personnel identify important keywords and see if the incoming request is similar to any historic incident. Otherwise, an incident ticket is created and, with other related information, forwarded to incident's subject matter expert (SME). Incident management process is used for managing the life cycle of all incidents. An organization spends lots of resources to keep its IT resources incident free and, therefore, timely resolution of incoming incident is required to attain that objective. Currently, the incident management process is largely manual, error prone and time consuming. In this paper, we use information integration techniques and machine learning to automate various processes in the incident management workflow. We give a method for correlating the incoming incident with configuration items (CIs) stored in Configuration management database (CMDB). Such a correlation can be used for correctly routing the incident to SMEs, incident investigation and root cause analysis. In our technique, we discover relevant CIs by exploiting the structured and unstructured information available in the incident ticket. We present efficient algorithm which gives more than 70% improvement in accuracy of identifying the failing component by efficiently browsing relationships among CIs.