关于大数据中需求服务信息快速检索仿真

摘要

对大数据中需求服务信息的检索,能够有效提高大数据信息利用率.对需求服务信息的快速检索,需要计算需求服务信息集聚类中心的关联度,设定平均关联度的调节系数,完成大数据中需求服务信息的检索.传统方法首先细分信息分类边缘,利用加载辨别函数对信息进行定位,但忽略了计算信息集聚类中心的关联度,导致检索精度偏低.提出基于粒度原理的需求服务信息快速检索方法.首先将检索需求概括成目标需求服务信息集,利用需求服务信息集间的关联度确定聚类中心,然后计算需求服务信息集聚类中心和所有需求服务信息集的平均关联度,设定平均关联度的调节系数,最后以确定检索空间内的需求服务信息满意度分量取值范围完成需求服务信息快速检索.实验结果表明,上述方法提高了需求服务信息检索的查准率、需求服务信息检索满意度以及检索结果利用率.%This paper proposes a quick search method of demand service information based on granularity theory.Firstly,the search requirement is summarized into information set of target requirement service,and clustering center is determined by using correlation degree between information sets of requirement service,and then average correlation degree is calculated between clustering center of requirement service information sets and information sets of all requirement service and accommodation coefficient of average correlation degree is set up.Finally,the quick search of requirement service information is completed according to determining value range of satisfaction degree component of demand service information in search space.The simulation results show that the proposed method improves precision ration and satisfaction degree of information search of demand service,and availability of search results.