A NMF-Based Learning of Topics and Clusters for IT Maintenance Tickets Aided by Heuristic

Ticketing system is designed to handle huge volumes of tickets generated by large enterprise IT infrastructure components. As huge amount of information remain tacit in the ticket data, topic derivation from them is essential to extract information automatically to gain insights for improving operational efficiency in IT governance. Because tickets are short and sparse by nature existing topic extraction methods cannot be suitably applied to them to learn reliable topics (and thus clusters). In this paper, we propose a novel way to address this problem which incorporates similarity between ticket contents as well as some heuristic. Topic derivation is done through a 2-stage matrix factorization process applied to ticket-ticket and ticket-term matrices. Further tickets are assigned to a topic (and the corresponding cluster) based on the derived dependence of tickets on topics and terms using some heuristic. We experiment our approach with industrial ticket data from several domains. The experimental results demonstrate that our method performs better than other popular modern topic learning methods. Also the use of heuristic in clustering does not affect the labeling of topics as the same set of labels are reproduced with similar scores.