Performance optimization of analysis rules in real-time active data warehouses

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Abstract

Analysis rule is an important component of a real-time active data warehouse. Performance optimization of analysis rules may greatly improve the system response time when a new event occurs. In this paper, we carry out the optimization work through the following three ways: (1)initiating non-real-time analysis rules as less as possible during rush hour of real-time analysis rules; (2) executing non-real-time analysis rules using the same cube at the same time interval; and (3) preparing frequent cubes for the use of real-time analysis rules ahead of time. We design the LADE system to get all the reference information required by optimization work. A new algorithm, called ARPO, is proposed to carry out the optimization work. Empirical studies show that our methods can effectively improve the performance of analysis rules. © 2012 Springer-Verlag Berlin Heidelberg.

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Lin, Z., Zhang, D., Lin, C., Lai, Y., & Zou, Q. (2012). Performance optimization of analysis rules in real-time active data warehouses. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7235 LNCS, pp. 669–676). https://doi.org/10.1007/978-3-642-29253-8_63

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