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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31106

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Multidimensional Performance Management
In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
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[1] S. Chaudhuri, G. Weikum. Rethinking Database System Architecture: Towards a Self-tuning RISC style Database System. In Proceedings of VLDB, 2000.
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[3] Alur, N; Balaji, R.; Miskimen, M.; Stolz-Hofmann, D.: IBM DB2 UDB Performance Expert for Multiplatforms - A usage guide. Redbook. IBM Corp, 2003.
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[17] Wiese, D.; Rabinovitch, G.; Reichert, M. and Arenswald, S.: Autonomic Tuning Expert - A framework for best-practice oriented autonomic database tuning. In Proceedings of Centre for Advanced Studies on Collaborative Research (CASCON 2008). Ontario, Canada, October 27 - 30, 2008.
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