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首页> 外文期刊>International journal of knowledge and web intelligence >Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases
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Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases

机译：远程空间数据库上集合k最近邻查询的近似搜索算法

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Searching Aggregate k-Nearest Neighbour (k-ANN) queries on remote spatial databases suffers from a large amount of communication. In order to overcome the difficulty, RQP-M algorithm for efficiently searching £-ANN query results is proposed in this paper. It refines query results originally searched by RQP-S with subsequent A-NN queries, whose query points are chosen among vertices of a regular polygon inscribed in a circle searched previously. Experimental results show that precision of sum A:-NN query results is over 0.95 and Number of Requests (NOR) is at most 4.0. On the other hand, precision of max k-NN query results is over 0.95 and NOR is at most 5.6. RQP-M brings 0.04-0.20 increase in PRECISION of sum k-NN query results and over 0.40 increase in that of max k-NN query results, respectively, in comparison with RQP-S.
机译：在远程空间数据库上搜索聚合k最近邻（k-ANN）查询存在大量通信。为了克服这一困难，本文提出了一种有效搜索£-ANN查询结果的RQP-M算法。它将RQP-S最初搜索的查询结果与随后的A-NN查询一起优化，其查询点是从刻在先前搜索的圆中的规则多边形的顶点中选择的。实验结果表明，总和A：-NN查询结果的精度超过0.95，请求数（NOR）最多为4.0。另一方面，最大k-NN查询结果的精度超过0.95，NOR最多为5.6。与RQP-S相比，RQP-M的总k-NN查询结果的精度提高了0.04-0.20，最大k-NN查询结果的精度提高了0.40以上。

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