分布式数据库分片关系变换自适应查询技术研究

Research on adaptive query technology of distributed database partition relational transformation

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.