Abstract
Today’s enterprise services and business applications are often centralized in a small number of data centers. Employees located at branches and side offices access the computing infrastructure via the internet using thin client architectures. The task to provide a good application quality to the employers using a multitude of different applications and access networks has thus become complex. Enterprises have to be able to identify resource bottlenecks and applications with a poor performance quickly to take appropriate countermeasures and enable a good application quality for their employees. Ticketing systems within an enterprise use large databases for collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. However, manual categorization of tickets comes with a high workload.
In this paper, we analyze in a case study the applicability of supervised learning algorithms for the automatic identification of relevant tickets, i.e., tickets indicating problematic applications. In that regard, we evaluate different classification algorithms using 12,000 manually annotated tickets accumulated in July 2013 at the ticketing system of a nation-wide operating enterprise. In addition to traditional machine learning metrics, we also analyze the performance of the different classifiers on business-relevant metrics.
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Due to space constraints we do not report on initial experiments performed for parameter optimization.
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This work is supported by the Deutsche Forschungsgemeinschaft (DFG) under Grants HO TR 257/41-1. The authors alone are responsible for the content.
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Zinner, T., Lemmerich, F., Schwarzmann, S., Hirth, M., Karg, P., Hotho, A. (2015). Text Categorization for Deriving the Application Quality in Enterprises Using Ticketing Systems. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_25
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