This paper presents a nearest base-neighbor (NBN) search that can be applied to a clustered nearest neighbor problem on spatial datasets with static properties. Given two sets of data points R and S, a query point q, distance threshold δ and cardinality threshold k, the NBN query retrieves a nearest point r (called the base-point) in R where more than k points in S are located within the distance δ. In this paper, we formally define a base-point and NBN problem. As the brute-force approach to this problem in massive datasets has large computational and I/O costs, we propose in-memory and external memory processing techniques for NBN queries. In particular, our proposed in-memory algorithms are used to minimize I/Os in the external memory algorithms. Furthermore, we devise a solution-based index, which we call the neighborhood-augmented grid, to dramatically reduce the search space. A performance study is conducted both on synthetic and real datasets. Our experimental results show the efficiency of our proposed approach.

中文翻译：

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空间数据集上的最近邻邻搜索

本文提出了一种可应用于具有静态属性的空间数据集上的聚类最近邻问题的最近邻（NBN）搜索。给出两组数据点- [R和小号，查询点q，距离阈值δ和基数阈ķ，所述NBN查询检索最近点- [R中（称为基点）- [R其中多于ķ在点小号位于内距离δ。在本文中，我们正式定义了一个基点和NBN问题。由于在海量数据集中解决该问题的蛮力方法具有大量的计算和I / O成本，因此我们为NBN查询提出了内存和外部内存处理技术。特别是，我们提出的内存中算法用于最小化外部存储器算法中的I / O。此外，我们设计了一种基于解决方案的索引，我们将其称为邻域增强网格，以显着减少搜索空间。对综合数据集和实际数据集均进行了性能研究。我们的实验结果表明了我们提出的方法的有效性。