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.
Supported by the Fundamental Research Funds for the Central Universities under Grant No. 2011121049, the Natural Science Foundation of Fujian Province of China under Grant No. 2011J05158 and 2011J05156, and the Natural Science Foundation of China under Grant No. 61001013 and 61102136.
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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: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_63
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DOI: https://doi.org/10.1007/978-3-642-29253-8_63
Publisher Name: Springer, Berlin, Heidelberg
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