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- W4251702553 abstract "In cloud computing environments parallel kNN queries for big data is an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operator widely adopted by many applications including knowledge discovery, data mining, and spatial databases. This chapter proposes a parallel method of kNN queries for big data using MapReduce programming model. Firstly, this chapter proposes an approximate algorithm that is based on mapping multi-dimensional data sets into two-dimensional data sets, and transforming kNN queries into a sequence of two-dimensional point searches. Then, in two-dimensional space this chapter proposes a partitioning method using Voronoi diagram, which incorporates the Voronoi diagram into R-tree. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on R-tree using MapReduce programming model. Finally, this chapter presents the results of extensive experimental evaluations which indicate efficiency of the proposed approach." @default.
- W4251702553 created "2022-05-12" @default.
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- W4251702553 date "2016-01-01" @default.
- W4251702553 modified "2023-09-25" @default.
- W4251702553 title "Parallel kNN Queries for Big Data Based on Voronoi Diagram Using MapReduce" @default.
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- W4251702553 doi "https://doi.org/10.4018/978-1-4666-9845-1.ch029" @default.
- W4251702553 hasPublicationYear "2016" @default.
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