Deriving the Employee-Perceived Application Quality in Enterprise IT Infrastructures using Information from Ticketing Systems


The need for a less complex maintenance of applications and the IT infrastructure for huge enterprises lead to the centralization of applications and services within data centers. Employees at sites and branches are connect to data centers via the Internet using a thin-client architectures resulting in additional failure sources beside the end devices, namely the transport network and hardware components in the data centers. To provide a good application quality to the employers using a multitude of different applications and access networks has thus become a complex task [2]. In order to evaluate the quality of an application, subjective metrics like Quality of Experience (QoE) [1] are often used. Ongoing research in the field of QoE typically tries to understand the impact of technical systems on the subjective perception of specific applications. Main influence factors are deduced and appropriate models allowing an estimation of the QoE for varying parameters like bandwidth, packet loss, or jitter are developed. The QoE for applications like web browsing, video streaming, VoIP, and office products are well understood. This, however, does not hold for enterprise applications like resource planning and management or data warehouse applications, which are not covered by current research. Time-consuming user surveys in the employers working environment highly affect the day-to-day business and thus are not practicable. Nevertheless, a profound knowledge of the application quality and availability is required to enable good conditions of work and a high working efficiency. For that, enterprises may rely on support systems like a hotline or a ticketing system. Particular the latter is a huge database collecting complaints and problems of the users over a long period of time and thus are an interesting starting point to identify performance problems. Using this data source, we propose an approach to automatically identify tickets indicating problematic applications and reflecting the user experience. To this end, our approach first groups similar tickets and afterwards tags the resulting groups with adequate keywords. For the grouping process, we rely on the information from the free-text fields of the tickets, which include a summary and a detailed description of the reported issue, and calculate the lexicographical distance between the tickets using the Jaccard index [3]. The keywords for the


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