基于查询聚类的物化视图动态选取策略
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摘要
针对物化视图集动态调整存在的"抖动性"问题,提出一种基于查询聚类的物化视图动态选取策略QCDMVS。以关联规则算法为依据计算属性字段相似性,计算查询语句相似性并对一个查询周期内的查询语句集进行聚类,产生候选视图,根据效益模型计算候选视图的效益,运用物化视图动态调整算法DSMVQC生成最终物化视图。以航空公司机票结算数据作为实验数据集,实验结果表明,相比基准算法,该改策略的运用使得数据仓库的查询性能有较大改善。
Aiming at the frequent jitter phenomenon exists in the immediate adjustment algorithm for materialized views set,a query clustering-based dynamic materialized views selection strategy(QCDMVS)was proposed.Attribute similarity was calculated based on the method of association rules,query similarity was calculated and candidate views set was generated by clustering the queries set during a statistical time,and the benefits of the candidate views were calculated according to the benefit model.The materialized views were selected using dynamic management algorithm of materialized views(DSMVQC).Experimental results based on the airline ticket settlement data sets show that,compared with the benchmark algorithm,the proposed strategy improves the query response performance of the data warehouse obviously.
Aiming at the frequent jitter phenomenon exists in the immediate adjustment algorithm for materialized views set,a query clustering-based dynamic materialized views selection strategy(QCDMVS)was proposed.Attribute similarity was calculated based on the method of association rules,query similarity was calculated and candidate views set was generated by clustering the queries set during a statistical time,and the benefits of the candidate views were calculated according to the benefit model.The materialized views were selected using dynamic management algorithm of materialized views(DSMVQC).Experimental results based on the airline ticket settlement data sets show that,compared with the benchmark algorithm,the proposed strategy improves the query response performance of the data warehouse obviously.
引文
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[3]RU Bei,HE Xinzheng.Efficient complete frequent itemsets mining algorithm of data stream[J].Computer Engineering and Design,2017,38(10):2759-2766(in Chinese).[茹蓓,贺新征.高效的数据流完全频繁项集挖掘算法[J].计算机工程与设计,2017,38(10):2759-2766.]
[4]SHI Liang,QIAN Xuezhong.Constrained frequent itemsets mining algorithm based on MapReduce[J].Computer Engineering and Design,2015,36(10):2725-2728(in Chinese).[施亮,钱雪忠.基于MapReduce的约束频繁项集挖掘算法[J].计算机工程与设计,2015,36(10):2725-2728.]
[5]ZHANG Jiang.A dynamic adjustment strategy of materialized views based on query clustering[D].Tianjin:Civil Aviation University of China,2018(in chinese).[张江.基于查询聚类的物化视图动态选取策略研究[D].天津:中国民航大学,2018.]
[6]YANG Xinxin,HUANG Shaobin.A hierarchical co-clustering algorithm for high-order heterogeneous data[J].Journal of Computer Research and Development,2015,52(1):200-210(in Chinese).[杨欣欣,黄少滨.高阶异构数据层次联合聚类算法[J].计算机研究与发展,2015,52(1):200-210.]
[7]WANG Jiateng, YIN Hong,XIE Wenbin,et al.Terrain mesh simplification based on vertex importance and hierarchical clustering tree[J].Computer Engineering and Design,2016,37(6):1543-1548(in Chinese).[王家腾,殷宏,解文彬,等.基于顶点重要度和层次聚类树的地形网格简化[J].计算机工程与设计,2016,37(6):1543-1548.]
[8]Narasimhan H,Kashyap R,Vikram A V,et al.Greedy search algorithm based two stage energy saving technique for dense LTE-A HetNets[C]//International Conference on Advances in Computing,Communications and Informatics,2017:894-899.
[9]FENG Xia,ZHANG Jiang,ZUO Haichao.A dynamic adjustment strategy of materialized views based on query clustering[J].Journal of Beijing University of Posts and Telecommunications,2018,41(4):16-22(in Chinese).[冯霞,张江,左海超.一种基于查询聚类的物化视图动态调整策略[J].北京邮电大学学报,2018,41(4):16-22.]
[10]QU Changwen,FENG Qi,LI Tingjun.Multisensor CPHD filter with Gaussian mixture implementation based on divideconquer and greedy algorithm[J].Acta Electronica Sinica,2018,46(10):2472-2479(in Chinese).[曲长文,冯奇,李廷军.基于分治-贪心算法的高斯混合多观测站CPHD滤波器[J].电子学报,2018,46(10):2472-2479.]
[11]ZHU Changsheng,ZHU Fuxi,YANG Xiaolan.Hierarchical community structure based algorithm for influence maximization[J].Computer Engineering and Design,2017,38(3):561-565(in Chinese).[朱昌盛,朱福喜,阳小兰.基于层次化社区结构的影响最大化算法[J].计算机工程与设计,2017,38(3):561-565.]