Materialized Views Selection of Multi-Dimensional Data in Real-Time Active Data Warehouses
通过基于主动决策引擎日志的数据挖掘来找到分析规则的CUBE 使用模式,从而为多维数据实视图选择算法提供重要依据;在此基础上设计了3A 概率模型,并给出考虑CUBE 受访概率分布的视图选择贪婪算法PGreedy(probability greedy), 以及结合视图挽留原则的视图动态调整算法. 实验结果表明, 在实时主动数据仓库环境下,PGreedy 算法比BPUS(benefit per unit space)算法具有更好的性能. In this paper, data mining based on the log of active decision engine is introduced to find the CUBE using pattern of analysis rules, which can be used as important reference information for materialized views selection. Based on it, a 3A probability model is designed, and the greedy algorithm, called PGreedy (probability greedy), is proposed, which takes into account the probability distribution of CUBE. Also view keeping rule is adopted to achieve better performance for dynamic view adjusting. Experimental results show that PGreedy algorithm can achieve better performance than BPUS (benefit per unit space) algorithm in real-time active data warehouses environment.