An Efficient Nearest Neighbor Search Method for Spatial Keyword Query Processing

2018 ◽  
Author(s):  
A Sanjumol ◽  
GEORGE Reena Mary
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Keyword Query ◽  
Neighbor Search ◽  
Spatial Keyword Query

scholarly journals k-Nearest Neighbor Search based on Node Density in MANETs

2014 ◽  
Vol 10 (4) ◽  
pp. 385-405 ◽  
Author(s):  
Yuka Komai ◽  
Yuya Sasaki ◽  
Takahiro Hara ◽  
Shojiro Nishio
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Node Density ◽  
Neighbor Search ◽  
Query Result ◽  
Knn Query ◽  
The One ◽  
K Nearest Neighbor Search

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

Bregman Hyperplane Trees for Fast Approximate Nearest Neighbor Search

2012 ◽  
Vol 3 (4) ◽  
pp. 75-87
Author(s):  
Bilegsaikhan Naidan ◽  
Magnus Lie Hetland
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Data Sets ◽  
Search Performance ◽  
Space Partitioning ◽  
Neighbor Search ◽  
Order Of Magnitude ◽  
Exact Nearest Neighbor

This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original.

scholarly journals A Radial Search Method for Fast Nearest Neighbor Search on Range Images

2016 ◽  
pp. 563-577
Author(s):  
Federico Tombari ◽  
Samuele Salti ◽  
Luca Puglia ◽  
Giancarlo Raiconi ◽  
Luigi Di Stefano
Keyword(s):  
Nearest Neighbor ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Range Images ◽  
Neighbor Search

Acceleration of Fractal Image Compression by Fast Nearest-Neighbor Search

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 231-241
Author(s):  
Leszek Ciepliński ◽  
Czesław Jȩdrzejek ◽  
Tomasz Major
Keyword(s):  
Image Compression ◽  
Nearest Neighbor ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Block Matching ◽  
Fractal Image Compression ◽  
Neighbor Search ◽  
Fractal Image ◽  
Partial Distortion ◽  
The Impact

In this paper we investigate the effect of fast nearest neighbor search method on acceleration of fractal image compression. First we follow the Saupe1 encoding step of fractal image compression that uses the multi-dimensional nearest neighbor search in a projected space. Then we investigate performance of a method for finding the nearest vector called partial distortion elimination. We also propose some supplementary accelerating concepts. For all inspected methods the impact of the tolerance criterion for mean square error of block matching is examined.

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