摘要
对物联网多源异构型的目标数据进行优化查询,能够有效提高查询数据的准确性。对多源异构型目标数据优化查询,需要得到个数据属性值相似度判别阈值,计算相似度总值,完成多源异构型目标数据优化查询。传统方法首先确定数据块边界,定义不同数据属性,但忽略了计算相似度总值,导致查询准确性低。提出动态权重的物联网多源异构型目标数据优化查询方法。对多源异构型目标数据质量进行度量,求得数据列间的灰色关联度,赋值函数得到目标数据的属性值和相似度判别阈值,计算其总值,完成目标数据优化查询。仿真证明,该方法大大提高了数据查询的准确性。
This paper proposes a method for optimization query of target data with multi - source heterogeneous type of internet of things based on dynamic weight. The quality of the target data is measured, and then grey correlation degree among data columns is obtained. The function is assigned to obtain attribute value and discrimination threshold of similarity degree of the target data, and its total value is calculated. Thus, the optimization query is completed. Simulation proves that the method improves accuracy of the data query greatly.
出处
《计算机仿真》
北大核心
2017年第12期435-438,共4页
Computer Simulation
基金
陕西省国际科技合作与交流项目(2016kw_045)
关键词
多源异构型
目标数据
优化查询
Multi- source heterogeneous type
Target data
Optimization query
