Analytics for similarity matching of IT cases with collaboratively-defined activity flows

Handling IT support cases efficiently is very important for operational excellence of IT organizations. Many IT service centers receive thousands of cases per day, some of which are similar to previously reported cases. To improve efficiency it is important to build upon lessons learned from past cases in the resolution of new cases. Therefore, a desired functionality of case management tools is finding similar previous cases to an open one, in order to leverage information about previous cases to effectively find resolution. A new generation of tools for IT case management, e.g., IT Support Conversation Manager, enables collaborative and adaptive process definition for IT case resolution. Leveraging collaborative and social networking technology makes the case information model increasingly richer and more structured compared to flat textual format case reports in traditional IT case management systems. We have developed an automated method for matching IT support cases that takes into account multiple information attributes including the collaborative flow of activities during case handling. We evaluated the system and the early evaluation results show that this method achieves a higher accuracy and comparable efficiency to text-based similarity approaches.