A statistical machine learning approach for ticket mining in IT service delivery

Abstract

Ticketing is a fundamental management process of IT service delivery. Customers typically express their requests in the form of tickets related to problems or configuration changes of existing systems. Tickets contain a wealth of information which, when connected with other sources of information such as asset and configuration information, monitoring information, can yield new insights that would otherwise be impossible to gain from one isolated source. Linking these various sources of information requires a common key shared by these data sources. The key is the server names. Unfortunately, due to historical as well as practical reasons, the server names are not always present in the tickets as a standalone field. Rather, they are embedded in unstructured text fields such as abstract and descriptions. Thus, automatically identifying server names in tickets is a crucial step in linking various information sources. In this paper, we present a statistical machine learning method called Conditional Random Field (CRF) that can automatically identify server names in tickets with high accuracy and robustness. We then illustrate how such linkages can be leveraged to create new business insights. © 2013 IFIP.