The k-nearest neighbor (kNN) algorithm is a classic supervised machine learning algorithm. It is widely used in cyber-physical-social systems (CPSS) to analyze and mine data. However, in practical CPSS applications, the standard linear kNN algorithm struggles to efficiently process massive data sets. This paper proposes a distributed storage and computation k-nearest neighbor (D-kNN) algorithm. The D-kNN algorithm has the following advantages: First, the concept of k-nearest neighbor boundaries is proposed and the k-nearest neighbor search within the k-nearest neighbors boundaries can effectively reduce the time complexity of kNN. Second, based on the k-neighbor boundary, massive data sets beyond the main storage space are stored on distributed storage nodes. Third, the algorithm performs k-nearest neighbor searching efficiently by performing distributed calculations at each storage node. Finally, a series of experiments were performed to verify the effectiveness of the D-kNN algorithm. The experimental results show that the D-kNN algorithm based on distributed storage and calculation effectively improves the operation efficiency of k-nearest neighbor search. The algorithm can be easily and flexibly deployed in a cloud-edge computing environment to process massive data sets in CPSS.

中文翻译：

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基于分布式存储和计算k-最近邻算法的网络-物理-社会系统的云边缘计算

k-最近邻（kNN）算法是一种经典的监督机器学习算法。它广泛用于网络物理社会系统 (CPSS) 来分析和挖掘数据。然而，在实际的 CPSS 应用中，标准的线性 kNN 算法难以有效处理海量数据集。本文提出了一种分布式存储计算k-最近邻（D-kNN）算法。D-kNN算法具有以下优点：首先，提出了k-最近邻边界的概念，在k-最近邻边界内进行k-最近邻搜索，可以有效降低kNN的时间复杂度。其次，基于k-neighbor边界，超出主存储空间的海量数据集存储在分布式存储节点上。第三，该算法通过在每个存储节点上执行分布式计算来有效地执行 k 最近邻搜索。最后，进行了一系列实验来验证D-kNN算法的有效性。实验结果表明，基于分布式存储和计算的D-kNN算法有效提高了k-最近邻搜索的运行效率。该算法可以轻松灵活地部署在云边缘计算环境中，处理CPSS中的海量数据集。