摘要
为了提高物联网通信大数据库的运行速度与处理效率,提出一种物联网通信大数据库半结构化数据识别方法,通过计算大数据库内数据的稀疏度系数与划分系数子空间,获得不同种类数据的关联特性,根据半结构化数据结构得到该其储存节点坐标,使用主成分分析和德尔菲算法融合集成转换为综合加权法,赋予识别半结构化数据得到指标权重,根据Tanimoto系数度量半1结构化数据的相似性并优化参数,实现识别大数据库内的半结构化数据。实验证明,通过所提识别方法处理后的数据重写率大幅度降低,且识别结果与真实结果差值较小,具有一定实际应用价值。
A semi-structured data identification method of Internet of things communication large database is proposed in order to improve the operation speed and processing efficiency of Internet of things communication large database. For obtaining the association characteristics of different kinds of data, the sparsity coefficient of data in the large database was calculated, and the coefficient subspace was divided. Based on the semi-structured data structure, the coordinates of storage nodes were obtained. Principal component analysis and Delphi algorithm were used to fuse and integrate and converted into a comprehensive weighting method, which was used to identify the semi-structured data to get index weight. According to the Tanimoto coefficient, the similarity of semi-structured data was measured and the parameters were optimized to achieve the recognition of semi-structured data in large databases. The experiments show that the data processed by this method has a low rewriting rate and minor error value(the difference between the recognition result and the real result).
作者
孙学军
李长银
SUN Xue-jun;LI Chang-yin(Feixian Campus of Linyi University,Linyi Shandong 273400,China)
出处
《计算机仿真》
北大核心
2021年第11期323-326,共4页
Computer Simulation
关键词
物联网通信
大数据库
半结构化数据
数据识别
识别指标权重
Internet of things communication
Big database
Semi-structured data
Data identification
Identification index weight
