Net大数据库散布点的抗干扰优化查询算法

Algorithm of Optimized Anti-jamming Query for Net Large Database Spread Point

在Net环境下,大数据库的散布数据具有随机分布特征,干扰性较强,难以实现有效查询,研究优化查询算法对提高大数据库的数据调度和访问能力具有重要价值。提出一种基于大数据信息流集合划分机制与模式匹配的Net大数据库散布点的抗干扰优化查询算法。构建Net大数据库散布点数据采集模型,引入了融合特征空间的构架模式,通过数据信息流集合划分机制与模式匹配,得到大数据信息流异步层最小竞争异步递进值,实现抗干扰优化查询。结合Matlab和SQL Sever混合编程进行仿真,实验结果表明,能有效提高对Net大数据库散布点的查询性能,抗干扰能力强,提高对散布点数据的召回率,在数据库构建和应用中具有应用价值。

In the Net environment, the data of large database walk with random distribution, the interference is strong, it is difficult to achieve effective query, query optimization algorithm research has important value for improving the data sched?uling and access of large database. Put forward scatter anti-jamming algorithm of query optimization point of a large data set partition information flow mechanism and pattern matching based on Net database. Construction of Net database spread model of point data acquisition, introduces the frame mode of fusion of the feature space, the set partitioning mechanism and pattern matching information through data flow, information flow to obtain large data asynchronous layer the smallest competitive asynchronous progressive values, anti jamming of query optimization. Simulation combined with the mixed pro?gramming of Matlab and SQLSever, the experimental results show that can effectively improve the query performance of Net large database distribution points, strong anti-interference ability, to improve the recall rate scatter data, has application value in the database construction and application.