摘要
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.
出处
《软件学报》
EI
CSCD
北大核心
2008年第2期301-313,共13页
Journal of Software
基金
Supported by the National Natural Science Foundation of China under Grant No.60473051 (国家自然科学基金)
the China HP Co. and Peking University Joint Project (北京大学-惠普(中国)合作项目)
关键词
视图选择
实视图
数据仓库
主动决策引擎
分析规则
联机分析处理
view selection
materialized view
data warehouse
active decision engine
analysis rule
OLAP （online analytical processing）
