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Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce
Abstract
Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool 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 parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.
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