Aggregate k Nearest Neighbor Queries in Metric Spaces

2018 ◽  
pp. 317-333
Author(s):  
Xin Ding ◽  
Yuanliang Zhang ◽  
Lu Chen ◽  
Keyu Yang ◽  
Yunjun Gao
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Queries

scholarly journals Distributed k-Nearest Neighbor Queries in Metric Spaces

2018 ◽  
pp. 236-252 ◽  
Author(s):  
Xin Ding ◽  
Yuanliang Zhang ◽  
Lu Chen ◽  
Yunjun Gao ◽  
Baihua Zheng
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Queries

scholarly journals Practical Construction of k-Nearest Neighbor Graphs in Metric Spaces

2006 ◽  
pp. 85-97 ◽  
Author(s):  
Rodrigo Paredes ◽  
Edgar Chávez ◽  
Karina Figueroa ◽  
Gonzalo Navarro
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

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.

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