Unfortunately, these fixed fields are typically of limited use, and the most useful information about the trouble and its resolution is captured in free text form by the human operators "working the trouble." Extracting useful information from trouble tickets is therefore an extremely challenging problem. Trouble tickets are often structured documents, with a certain number of fixed fields identifying, for example, the time of the event and the manner in which the trouble was detected. As such, trouble tickets contain a wealth of information about the network and the problems it experiences over time. In dealing with network problems, network operators often make use of so-called "trouble tickets." These trouble tickets get created when the network problem is first identified and then serve as a means to document the investigation into the problem, and, hopefully, the resolution thereof. We have used NetSieve in several key network operations: analyzing device failure trends, understanding why network redundancy fails, and identifying device problem symptoms. Our results show that NetSieve achieves 89%-100% accuracy and its inference output is useful to learn global problem trends. We evaluate NetSieve on 10K+ tickets from a large cloud provider, and compare its accuracy using (a) an expert review, (b) a study with operators, and (c) vendor data that tracks device replacement and repairs. To cope with ambiguity in free-form text, NetSieve leverages learning from human guidance to improve its inference accuracy. Our system, NetSieve, combines statistical natural language processing (NLP), knowledge representation, and ontology modeling to achieve these goals. This paper takes a practical step towards automatically analyzing natural language text in network tickets to infer the problem symptoms, troubleshooting activities and resolution actions. Unfortunately, while tickets carry valuable information for network management, analyzing them to do problem inference is extremely difficult-fixed fields are often inaccurate or incomplete, and the free-form text is mostly written in natural language. Network trouble tickets are diaries comprising fixed fields and free-form text written by operators to document the steps while troubleshooting a problem. This paper presents NetSieve, a system that aims to do automated problem inference from network trouble tickets.
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