Automatic Structuring of IT Problem Ticket Data for Enhanced Problem Resolution

In this paper we propose a novel technique to automatically structure problem tickets consisting of free form, heterogeneous textual data, so that IT problem isolation and resolution can be performed rapidly. The originality of our technique consists in applying the conditional random fields (CRFs) supervised learning process to automatically identify individual units of information in the raw data. The CRFs have been shown to be effective on real-world tasks in various fields. We apply our technique to identify structural patterns specific to the problem ticket data used in call centers to enhance the problem resolution system used by remote technical assistance personnel. Most of the existing ticketing data is not explicitly structured, is highly noisy, and very heterogeneous in content, making it hard to effectively apply common data mining techniques to analyze and search the raw data. An example of such an analysis is the detection of the units of information containing the steps taken by the technical people to resolve a particular customer issue. We present a study of the accuracy of our results.