scholarly journals Efficient Maximum Range Search on Remote Spatial Databases Using k-Nearest Neighbor Queries

2013 ◽  
Vol 22 ◽  
pp. 836-845 ◽  
Author(s):  
Hideki Sato ◽  
Ryoichi Narita
Keyword(s):  
Spatial Databases ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Range Search ◽  
Maximum Range ◽  
Nearest Neighbor Queries

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

2016 ◽  
pp. 163-182
Author(s):  
Wei Yan
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Parallel Queries ◽  
Massive Spatial Data ◽  
Nearest Neighbor Queries

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.

Parallel kNN Queries for Big Data Based on Voronoi Diagram Using MapReduce

2015 ◽  
pp. 392-414
Author(s):  
Wei Yan
Keyword(s):  
Big Data ◽  
Voronoi Diagram ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
Dimensional Space ◽  
Data Sets ◽  
Two Dimensional ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

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.

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