Processing Continuous k Nearest Neighbor Queries in Obstructed Space with Voronoi Diagrams

2020 ◽  
Vol 7 (2) ◽  
pp. 1-27
Author(s):  
Huaijie Zhu ◽  
Xiaochun Yang ◽  
Bin Wang ◽  
Wang-Chien Lee ◽  
Jian Yin ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Voronoi Diagrams ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Queries

scholarly journals Grid-based k-Nearest Neighbor Queries over Moving Object Trajectories with MapReduce

2017 ◽  
Vol 10 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Ying Xia ◽  
Ruidi Wang ◽  
Xu Zhang ◽  
Hae Young Bae
Keyword(s):  
Nearest Neighbor ◽  
Moving Object ◽  
K Nearest Neighbor ◽  
Object Trajectories ◽  
Nearest Neighbor Queries ◽  
Grid Based ◽  
Moving Object Trajectories

Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries

2010 ◽  
pp. 233-249 ◽  
Author(s):  
Mahady Hasan ◽  
Muhammad Aamir Cheema ◽  
Wenyu Qu ◽  
Xuemin Lin
Keyword(s):  
Nearest Neighbor ◽  
Efficient Algorithms ◽  
K Nearest Neighbor ◽  
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.

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