Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Efficient ticket routing by resolution sequence mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
EasyTicket: a ticket routing recommendation engine for enterprise problem resolution
Proceedings of the VLDB Endowment
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Next best step and expert recommendation for collaborative processes in it service management
BPM'11 Proceedings of the 9th international conference on Business process management
Privacy protected knowledge management in services with emphasis on quality data
Proceedings of the 20th ACM international conference on Information and knowledge management
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
| Hi-index | 0.00 |
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.