Published Paper Details:

These days IT service providers are rapidly embracing an automated services delivery model in order to keep pace with advances in technology and demanding market pressure to reduce and maintain quality. Application development and maintenance is a good example of a service system in which a sizable volume of tickets are raised every day for different issues to get resolved with a view to deliver uninterrupted service. An issue is captured as summary on the ticket and once a ticket is resolved, the solution is also noted down on the ticket as resolution. It will be bene?cial to automatically extract information from the description of tickets to improve operations like identifying critical and frequent issues, grouping of tickets based on textual content, suggesting remedial measures for them etc. In particular, the maintenance people can save a lot of effort and time if they have access to past remedial actions for similar kind of tickets raised earlier based on history data. In this work we propose an automated method based on background knowledge of tickets for recovering resolutions for fresh tickets using unsupervised learning and the traditional FULL TEXT SEARCH algorithm.