分布式数据库分片关系变换自适应查询技术研究
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摘要
分布式数据库查询过程中受到数据片之间的耦合关联的影响,容易出现查询输出冗余误差,为了提高查询准确性,提出一种基于分片关系变换的分布式数据库自适应查询技术。构建分布式数据库的分簇聚类模型,采用数据关系集的本体特征融合方法进行数据库的状态向量空间组合,在组合的状态矢量空间中进行数据集的分片处理,根据分片属性进行自适应特征分解和耦合关系变换,提取待查询数据集的互信息熵特征量,根据提取的互信息熵特征量的后置聚集性进行数据查询,输出准确的查询数据集。仿真结果表,采用该方法进行分布式数据库查询的准确性较好,数据查准率和自适应性能较优。
The distributed database query process is affected by the coupling association between the pieces of data,and it is easy to appear redundant error of query output,in order to improve the accuracy of the query,A distributed database adaptive query technology based on piecewise relational transformation is proposed. The clustering model of distributed database is constructed. The ontology feature fusion method of data relational set is used to combine the state vector space of the database. In the combined state vector space,the data sets are sliced,and adaptive feature decomposition and coupling relation transformation are carried out according to the piecewise attributes,and the mutual information entropy feature quantity of the data set to be queried is extracted. According to the post-aggregation of the extracted mutual information entropy feature quantity,the data query is carried out,and the accurate query data set is outputted. The simulation result table shows that the method is more accurate in the distributed database query. Data precision and adaptive performance are better.
The distributed database query process is affected by the coupling association between the pieces of data,and it is easy to appear redundant error of query output,in order to improve the accuracy of the query,A distributed database adaptive query technology based on piecewise relational transformation is proposed. The clustering model of distributed database is constructed. The ontology feature fusion method of data relational set is used to combine the state vector space of the database. In the combined state vector space,the data sets are sliced,and adaptive feature decomposition and coupling relation transformation are carried out according to the piecewise attributes,and the mutual information entropy feature quantity of the data set to be queried is extracted. According to the post-aggregation of the extracted mutual information entropy feature quantity,the data query is carried out,and the accurate query data set is outputted. The simulation result table shows that the method is more accurate in the distributed database query. Data precision and adaptive performance are better.
引文
[1]MORADI M,KEYVANPOUR M R.An analytical review of XML association rules mining[J].Artificial Intelligence Review,2015,43(2):277-300.
[2]DONG G L,RYU K S,BASHIR M,et al.Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J].Journal of Medical Systems,2013,37(2):1-10.
[3]KHALILI A,SAMI A.Sys Detect:a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm[J].Journal of Process Control,2015,2776:154-160.
[4]KESHAVAMURTHY B N,KHAN A M,TOSHNIWAL D.Privacy preserving association rule mining over distributed databases using genetic algorithm[J].Neural Computing&Applications,2013,22(Supplement 1):351-364.
[5]CZIBULA G,MARIAN Z,CZIBULA I G.Detecting software design defects using relational association rule mining[J].Knowledge and Information Systems,2015,42(3):545-577.
[6]BOUDIA O R M,SENOUCI S M,FEHAM M,A novel secure aggregation scheme for wireless sensor networks using stateful public key cryptography[J].Ad Hoc Networks,2015,32(C):98-113.
[7]CHEN S,WANG G,JIA W.Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph[J].Future Generation Computer Systems,2016,55:391-400.
[8]ZHANG Q Y,WANG R C,SHA C,et al.Node correlation clustering algorithm for wireless multimedia sensor networks based on overlapped Fo Vs[J].Journal of China Universities of Posts and Telecommunications,2013,20(5):37-44.
[9]LIU Z,YUAN Y,GUAN X,et al.An approach of distributed joint optimization for cluster-based wireless sensor networks[J].IEEE/CAA Journal of Automatica Sinica,2015,2(3):267-273.
[10]杨景明,侯宇浩,孙浩,赵志伟.采用数量级阈值与二维信息排序策略的NSGA-II-DE算法[J].控制与决策,2016,31(09):1577-1584.
[11]Stoean C,Preuss M,Stoean R,et al.Multimodal optimization by means of a topological species conservation algorithm[J].IEEE Trans on Evolutionary Computation,2010,14(6):842-864.
[12]Liang J J,Qu B Y,Mao X B,et al.Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization[J].Neurocomputing,2014,137(8):252-260.
[13]官亚勤,赵学胜,王鹏飞,等.基于切片原理的海量点云并行简化算法[J].计算机应用,2016,36(7):1793-1796.
[14]周煜,张万冰,杜发荣,等.散乱点云数据的曲率精简算法[J].北京理工大学学报,2010,30(7):785-790.
[15]马俊涛,高梅国,董健.基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法[J].电子与信息学报,2016,38(6):1431-1437.
[2]DONG G L,RYU K S,BASHIR M,et al.Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J].Journal of Medical Systems,2013,37(2):1-10.
[3]KHALILI A,SAMI A.Sys Detect:a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm[J].Journal of Process Control,2015,2776:154-160.
[4]KESHAVAMURTHY B N,KHAN A M,TOSHNIWAL D.Privacy preserving association rule mining over distributed databases using genetic algorithm[J].Neural Computing&Applications,2013,22(Supplement 1):351-364.
[5]CZIBULA G,MARIAN Z,CZIBULA I G.Detecting software design defects using relational association rule mining[J].Knowledge and Information Systems,2015,42(3):545-577.
[6]BOUDIA O R M,SENOUCI S M,FEHAM M,A novel secure aggregation scheme for wireless sensor networks using stateful public key cryptography[J].Ad Hoc Networks,2015,32(C):98-113.
[7]CHEN S,WANG G,JIA W.Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph[J].Future Generation Computer Systems,2016,55:391-400.
[8]ZHANG Q Y,WANG R C,SHA C,et al.Node correlation clustering algorithm for wireless multimedia sensor networks based on overlapped Fo Vs[J].Journal of China Universities of Posts and Telecommunications,2013,20(5):37-44.
[9]LIU Z,YUAN Y,GUAN X,et al.An approach of distributed joint optimization for cluster-based wireless sensor networks[J].IEEE/CAA Journal of Automatica Sinica,2015,2(3):267-273.
[10]杨景明,侯宇浩,孙浩,赵志伟.采用数量级阈值与二维信息排序策略的NSGA-II-DE算法[J].控制与决策,2016,31(09):1577-1584.
[11]Stoean C,Preuss M,Stoean R,et al.Multimodal optimization by means of a topological species conservation algorithm[J].IEEE Trans on Evolutionary Computation,2010,14(6):842-864.
[12]Liang J J,Qu B Y,Mao X B,et al.Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization[J].Neurocomputing,2014,137(8):252-260.
[13]官亚勤,赵学胜,王鹏飞,等.基于切片原理的海量点云并行简化算法[J].计算机应用,2016,36(7):1793-1796.
[14]周煜,张万冰,杜发荣,等.散乱点云数据的曲率精简算法[J].北京理工大学学报,2010,30(7):785-790.
[15]马俊涛,高梅国,董健.基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法[J].电子与信息学报,2016,38(6):1431-1437.
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