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Voronoi-Based kNN Queries Using K-Means Clustering in MapReduce

Voronoi-Based kNN Queries Using K-Means Clustering in MapReduce
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Author(s): Wei Yan (Liaoning University, China)
Copyright: 2019
Pages: 22
Source title: Emerging Technologies and Applications in Data Processing and Management
Source Author(s)/Editor(s): Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China) and Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-5225-8446-9.ch011

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Abstract

The kNN queries are special type of queries for massive spatial big data. The k-nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, are useful tools widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a method of kNN queries based on Voronoi diagram-based partitioning using k-means clusters in MapReduce programming model. Firstly, this chapter proposes a Voronoi diagram-based partitioning approach for massive spatial big data. Then, this chapter presents a k-means clustering approach for the object points based on Voronoi diagram. Furthermore, this chapter proposes a parallel algorithm for processing massive spatial big data using kNN queries based on k-means clusters in MapReduce programming model. Finally, extensive experiments demonstrate the efficiency of the proposed approach.

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