Efficient ticket routing by resolution sequence mining

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing--transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver. In this paper, we address the possibility of improving ticket routing efficiency by mining ticket resolution sequences alone, without accessing ticket content. To demonstrate this possibility, a Markov model is developed to statistically capture the right decisions that have been made toward problem resolution, where the order of the Markov model is carefully chosen according to the conditional entropy obtained from ticket data. We also design a search algorithm, called Variable-order Multiple active State search (VMS), that generates ticket transfer recommend...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Qihong Shao, Yi Chen, Shu Tao, Xifeng Yan, Nikos Anerousis
• Keywords: Data Mining, KDD 2008, Ticket Resolution Sequences, Ticket Routing Efficiency, Ticket Routing--transferring Problem