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障碍空间中不确定对象的组k最近邻查询方法
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  • 英文篇名:Group k-Nearest Neighbor Query Method for Uncertainty Data in Obstructed Spaces
  • 作者:万静 ; 唐贝贝 ; 孙健 ; 何云斌 ; 李松
  • 英文作者:WAN Jing;TANG Bei-bei;SUN Jian;HE Yun-bin;LI Song;School of Computer Science and Technology,Harbin University of Science and Technology;
  • 关键词:R树 ; 组最近邻查询 ; 不确定性 ; 可视性 ; 障碍距离
  • 英文关键词:R-tree;;group nearest neighbor query;;uncertainty;;visibility;;obstructed distance
  • 中文刊名:HLGX
  • 英文刊名:Journal of Harbin University of Science and Technology
  • 机构:哈尔滨理工大学计算机科学与技术学院;
  • 出版日期:2019-06-17 08:58
  • 出版单位:哈尔滨理工大学学报
  • 年:2019
  • 期:v.24
  • 基金:黑龙江省教育厅科学技术研究项目(12531z004)
  • 语种:中文;
  • 页:HLGX201903005
  • 页数:6
  • CN:03
  • ISSN:23-1404/N
  • 分类号:32-37
摘要
针对障碍空间中不确定对象的组k最近邻查询问题,提出了Pk OGNN(probabilistic k obstructed group nearest neighbor query)查询方法。Pk OGNN查询方法主要包括4个子算法:Compadist_o(),SpatialPru(),PruInterEnt()和PkOGNN(),这些子算法分别是集总障碍距离的计算方法、空间修剪方法、根据空间修剪方法进行R树中间结点修剪、最终精炼查询方法。所提Pk OGNN查询方法通过集成有效的修剪策略以便减少Pk OGNN的搜索空间,得到正确的k GNNs。理论研究和实验结果表明,所提方法具有较好的性能。
        To deal with the problem of group k-nearest neighbor query method for uncertainty data in obstructed spaces,this paper presents the method of the Pk OGNN( probabilistic k obstructed group nearest neighbor) query.The Pk OGNN query method mainly includes four sub-algorithms: Compadist _o(),SpatialPru(),PruInterEnt()and PkOGNN(),These algorithms are respectively the calculation of the aggregate obstructed distance,the spatial pruning method,the pruning of the R-tree intermediate items according to the spatial pruning method,the final refined query method. It integrates the effective pruning methods to reduce the search space of Pk OGNN and get the correct k GNNs. The theoretical research and experimental results show that the proposed method has good efficiency.
引文
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