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Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries

机译:V * -kNN的分析和评估:移动kNN查询的有效算法

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The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram, as well as analysis and evaluation of its associated algorithm, V*-kNN. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the knowledge of the query location and the boundary of the search space in addition to the data objects. As a result, V*-kNN has much smaller I/O and computation costs than existing methods. We further provide cost models to estimate the number of data accesses for V*-kNN and a competitive technique, RIS-kNN. The V*-Diagram and V*-kNN are also applicable to the domain of spatial networks and we present algorithms to construct a spatial-network V*-Diagram. Our experimental results show that V*-kNN significantly outperforms the competitive technique. The results also verify the accuracy of the cost models.
机译:移动k最近邻居(MkNN)查询连续查找移动查询点的k最近邻居。通过使用安全区域,可以有效地处理MkNN查询。通常,安全区域是查询点可以在其中更改而不更改查询答案的区域。本文提出了一种基于安全区域的增量式回答MkNN查询的技术,称为V * -Diagram,并对其相关算法V * -kNN进行了分析和评估。传统的安全区域方法基于数​​据对象但不依赖于查询位置来计算安全区域。除了数据对象之外,我们的方法还利用了查询位置和搜索空间边界的知识。结果,与现有方法相比,V * -kNN的I / O和计算成本要小得多。我们还提供了成本模型来估算V * -kNN和竞争技术RIS-kNN的数据访问次数。 V *-图和V * -kNN也适用于空间网络领域,我们提出了构建空间网络V *-图的算法。我们的实验结果表明,V * -kNN明显优于竞争技术。结果还验证了成本模型的准确性。

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