Content-aware resolution sequence mining for ticket routing

Peng Sun, Shu Tao, Xifeng Yan, Nikos Anerousis, Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


Ticket routing is key to the efficiency of IT problem management. Due to the complexity of many reported problems, problem tickets typically need to be routed among various expert groups, to search for the right resolver. In this paper, we study the problem of using historical ticket data to make smarter routing recommendations for new tickets, so as to improve the efficiency of ticket routing, in terms of the Mean number of Steps To Resolve (MSTR) a ticket. Previous studies on this problem have been focusing on mining ticket resolution sequences to generate more informed routing recommendations. In this work, we enhance the existing sequence-only approach by further mining the text content of tickets. Through extensive studies on real-world problem tickets, we find that neither resolution sequence nor ticket content alone is sufficient to deliver the most reduction in MSTR, while a hybrid approach that mines resolution sequences in a content-aware manner proves to be the most effective. We therefore propose such an approach that first analyzes the content of a new ticket and identifies a set of semantically relevant tickets, and then creates a weighted Markov model from the resolution sequences of these tickets to generate routing recommendations. Our experiments show that the proposed approach achieves significantly better results than both sequence-only and content-only solutions.

Original languageEnglish (US)
Title of host publicationBusiness Process Management - 8th International Conference, BPM 2010, Proceedings
PublisherSpringer Verlag
Number of pages17
ISBN (Print)3642156177, 9783642156175
StatePublished - 2010
Externally publishedYes
Event8th International Conference on Business Process Management, BPM 2010 - Hoboken, NJ, United States
Duration: Sep 13 2010Sep 16 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6336 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Business Process Management, BPM 2010
Country/TerritoryUnited States
CityHoboken, NJ

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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