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针对欧式空间中基于R树索引结构的反最近邻查询技术不适用于道路网环境,利用任意度量空间中的M树索引结构代替R树索引结构,进行道路网络中的反最近邻查询处理.然而,由于网络距离的计算代价高的问题,使得基于M树索引的反k最近邻查询效率很低.因此,采用道路网络嵌入技术,映射道路网络到高维向量空间,简单的L∞距离准确近似计算网络距离.在此基础上,提出道路网中近似反k最近邻查询的ARkNN算法,并对本文L∞距离近似网络距离的质量、k-中心聚类算法选取参考点的有效性和ARkNN算法的查询效率进行了实验验证.
The anti-nearest neighbor query technique based on R-tree index structure in European space is not suitable for road network environment, and the M-tree index structure in arbitrary metric space is used to replace the R-tree index structure to process the anti-nearest neighbor query in the road network. , The anti-k nearest neighbor query based on M-tree index is inefficient due to the high computational cost of network distance.Therefore, using the road network embedding technique, the road network is mapped into the high-dimensional vector space, and the simple L∞ distance is accurately approximated Based on which the ARkNN algorithm for approximation of anti-k nearest neighbor in road network is proposed, and the quality of L∞ distance approximation network distance, the validity of reference point selection by k-center clustering algorithm and ARkNN algorithm The query efficiency is verified experimentally.