An algorithm for suffix stripping
Readings in information retrieval
An Approach to Classify Software Maintenance Requests
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Efficient ticket routing by resolution sequence mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data 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
A Robust Discriminative Term Weighting Based Linear Discriminant Method for Text Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Priority-based routing with strict deadlines and server flexibility under uncertainty
Winter Simulation Conference
Automatic Classification of Change Requests for Improved IT Service Quality
SRII '11 Proceedings of the 2011 Annual SRII Global Conference
Privacy protected knowledge management in services with emphasis on quality data
Proceedings of the 20th ACM international conference on Information and knowledge management
Accelerating collaboration in task assignment using a socially enhanced resource model
BPM'13 Proceedings of the 11th international conference on Business Process Management
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In an IT service delivery environment, the speedy dispatch of a ticket to the correct resolution group is the crucial first step in the problem resolution process. The size and complexity of such environments make the dispatch decision challenging, and incorrect routing by a human dispatcher can lead to significant delays that degrade customer satisfaction, and also have adverse financial implications for both the customer and the IT vendor. In this paper, we present SmartDispatch, a learning-based tool that seeks to automate the process of ticket dispatch while maintaining high accuracy levels. SmartDispatch comes with two classification approaches - the well-known SVM method, and a discriminative term-based approach that we designed to address some of the issues in SVM classification that were empirically observed. Using a combination of these approaches, SmartDispatch is able to automate the dispatch of a ticket to the correct resolution group for a large share of the tickets, while for the rest, it is able to suggest a short list of 3-5 groups that contain the correct resolution group with a high probability. Empirical evaluation of SmartDispatch on data from 3 large service engagement projects in IBM demonstrate the efficacy and practical utility of the approach.