Domain-Driven Data Mining for IT Infrastructure Support

Support analytics (i.e., statistical analysis, modeling and mining of customer/operations support tickets data) is important in service industries. In this paper, we adopt a domain-driven data mining approach to support analytics with a focus on IT infrastructure Support (ITIS) services. We identify specific business questions and then propose algorithms for answering them. The questions are: (1) How to reduce the overall workload? (2) How to improve efforts spent in ticket processing? (3) How to improve compliance to service level agreements? We propose novel formalizations of these notions and propose rigorous statistics-based algorithms for these questions. The approach is domain-driven in the sense that the results produced are directly usable by and easy to understand for end-users having no expertise in data-mining, do not require any experimentation and often discover novel and non-obvious answers. All this helps in better acceptance among end-users and more active use of the results produced. The algorithms have been implemented and have produced satisfactory results on more than 25 real-life ITIS datasets, one of which we use for illustration.