分析现有反k近邻(reverse k nearest neighbor,RkNN)查询在效率、数据维度等方面的不足,提出基于R树结点覆盖值(R-tree’s cover-value)的RC-反k近邻查询方法.该方法需预先计算R树每个结点的覆盖值,采用过滤-精炼两步式处理方法,在过滤...分析现有反k近邻(reverse k nearest neighbor,RkNN)查询在效率、数据维度等方面的不足,提出基于R树结点覆盖值(R-tree’s cover-value)的RC-反k近邻查询方法.该方法需预先计算R树每个结点的覆盖值,采用过滤-精炼两步式处理方法,在过滤阶段采用两种剪枝启发式.该方法可有效处理数据库更新,适用于任意k值、任意维的对象集,查询结果精确,且计算量较小.实验结果表明,在k>6时RC-反k近邻查询时间比同类工作更短.展开更多
The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of place...The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of places, where the influence of a place is defined as the number of its bichromatic reverse k nearest neighbors （BRkNNs）. Two new metrics and their corresponding rules are introduced to shrink the search region and reduce the candidates of BRkNNs checked. Extensive experiments confirm that our proposed approach outperforms the state-of-the-art competitor significantly.展开更多
文摘分析现有反k近邻(reverse k nearest neighbor,RkNN)查询在效率、数据维度等方面的不足,提出基于R树结点覆盖值(R-tree’s cover-value)的RC-反k近邻查询方法.该方法需预先计算R树每个结点的覆盖值,采用过滤-精炼两步式处理方法,在过滤阶段采用两种剪枝启发式.该方法可有效处理数据库更新,适用于任意k值、任意维的对象集,查询结果精确,且计算量较小.实验结果表明,在k>6时RC-反k近邻查询时间比同类工作更短.
基金Supported by the National Natural Science Foundation of China (61003049)the Natural Science Foundation of Zhejiang Province (Y110278, 2010QNA5051)Zheda Zijin Plan
文摘The continuous top-t most influential place （CTtMIP） query is defined formally and solved efficiently in this paper. A CTtMIP query continuously monitors the t places with the maximum influence from the set of places, where the influence of a place is defined as the number of its bichromatic reverse k nearest neighbors （BRkNNs）. Two new metrics and their corresponding rules are introduced to shrink the search region and reduce the candidates of BRkNNs checked. Extensive experiments confirm that our proposed approach outperforms the state-of-the-art competitor significantly.
