Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
提出了一种快速的稀有类检测算法——CATION(rare category detection algorithm based on weightedboundary degree).通过使用加权边界度(weighted boundary degree,简称WBD)这一新的稀有类检测标准,该算法可利用反向k近邻的特性来寻...提出了一种快速的稀有类检测算法——CATION(rare category detection algorithm based on weightedboundary degree).通过使用加权边界度(weighted boundary degree,简称WBD)这一新的稀有类检测标准,该算法可利用反向k近邻的特性来寻找稀有类的边界点,并选取加权边界度最高的边界点询问其类别标签.实验结果表明,与现有方法相比,该算法避免了现有方法的局限性,大幅度地提高了发现数据集中各个类的效率,并有效地缩短了算法运行所需要的运行时间.展开更多
在外包空间数据库模式下,数据持有者委托第三方数据发布者代替它来管理数据并且执行查询.当发布者受到攻击或者由于自身的不安全性,它可能返回不正确的查询结果给用户.基于已有的反向k近邻(Reverse k Nearest Neighbor,RkNN)查询方法,...在外包空间数据库模式下,数据持有者委托第三方数据发布者代替它来管理数据并且执行查询.当发布者受到攻击或者由于自身的不安全性,它可能返回不正确的查询结果给用户.基于已有的反向k近邻(Reverse k Nearest Neighbor,RkNN)查询方法,采用将反向k近邻查询验证转化成k近邻查询验证和范围查询验证的思想,提出一种反向k近邻查询验证的方法,并且设计了相应的算法,用于验证返回给客户端结果的正确性(没有结果点被篡改),有效性(结果点都满足用户的查询要求)和完整性(没有遗漏符合查询要求的结果点).实验验证了算法的有效性和实用性.展开更多
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
文摘提出了一种快速的稀有类检测算法——CATION(rare category detection algorithm based on weightedboundary degree).通过使用加权边界度(weighted boundary degree,简称WBD)这一新的稀有类检测标准,该算法可利用反向k近邻的特性来寻找稀有类的边界点,并选取加权边界度最高的边界点询问其类别标签.实验结果表明,与现有方法相比,该算法避免了现有方法的局限性,大幅度地提高了发现数据集中各个类的效率,并有效地缩短了算法运行所需要的运行时间.
文摘在外包空间数据库模式下,数据持有者委托第三方数据发布者代替它来管理数据并且执行查询.当发布者受到攻击或者由于自身的不安全性,它可能返回不正确的查询结果给用户.基于已有的反向k近邻(Reverse k Nearest Neighbor,RkNN)查询方法,采用将反向k近邻查询验证转化成k近邻查询验证和范围查询验证的思想,提出一种反向k近邻查询验证的方法,并且设计了相应的算法,用于验证返回给客户端结果的正确性(没有结果点被篡改),有效性(结果点都满足用户的查询要求)和完整性(没有遗漏符合查询要求的结果点).实验验证了算法的有效性和实用性.
