基于不确定数据的频繁项查询算法  

Frequent Items Detection of Uncertain Data

作　　者：王爽[1] 杨广明[1] 朱志良[1] 

机构地区：[1]东北大学软件学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2011年第3期344-347,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60873011)

摘　　要：频繁项的查询是一项非常重要的技术,但在新兴的不确定数据领域却是一项新的研究课题.基于不确定数据,提出了一种新的频繁项定义,并提出了两条过滤规则,可以有效地减少检测数据的数量.最后提出高效的频繁项查询算法UFI,该算法通过找到概率求解中的递推规律,极大提高了单点检测效率.实验结果显示:提出的方法可以有效地减少候选集,降低搜索空间,改善在不确定数据上的查询性能.Frequent items detection has been an important feature of many applications,but it is a new area of research for emerging uncertain databases.A new definition of frequent items detection for uncertain data is proposed,thereby forming the basis for two efficient filtering rules that can significantly reduce the number of items to be detected.Furthermore,an efficient algorithm UFI is proposed to detect frequent items on uncertain databases.The UFI algorithm locates the recursive rule in the probability computation and greatly improves the efficiency of single data detection.These proposed methods can efficiently narrow the field of candidates and reduce corresponding searching space,thereby improving performance of query processing of uncertain data.

关 键 词：频繁项 不确定数据 剪枝规则 不确定数据模型 查询处理 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]