Web仓储中视图变化频率的自适应估测

物化视图的刷新是Web仓储进行系统维护的一项主要任务,而基础数据变化频率则是刷新方案中的重要因素.在已有文献中,研究者已经给出一些关于基础数据变化规律的算法和估测器.虽然这些估测器取得了不错的效果,然而他们却忽略了这些估测器都有一定的适用范围,超出这个范围则效果急剧下降.在此,基于泊松过程进行分析,对估测器的适用范围进行了讨论,根据估测结果的偏离值和有效性对估测公式进行参数调整,同时根据估测值的大小不断调整数据源的访问频率和次数,从而使数据源访问模式和估测器互相适应,使估测器在最佳估测范围内获得估测值.实验结果表明,与已有文献中的方法相比,新提出的自适应估测算法能够取得更好的效果.

Self-Adaptive Estimation of View Change Frequency in Web Warehouses

Refreshing materialized views is a main task of Web warehouse maintenance. As the refreshing scheme depends heavily on the base data change frequency, researchers have presented many corresponding algorithms and frequency estimators for it. Although these estimators really work, however, all of them have limitations. The bias that an estimator introduces will increase significantly when the estimated value is out of its applicable range. In this paper, a self-adaptive algorithm is presented based on Poisson process analysis, which can adjust the revisiting pattern and revisiting frequency according to the estimated change frequency. This algorithm can also tune the parameters so that the estimated value will fall into the best applicable range of the estimator. According to the experimental results, the proposed estimator is more accurate than the ones in the previous work.