| Summary: | 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.
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