Towards Efficient and Flexible KNN Query Processing in Real-Life Road Networks

2008 ◽  
Author(s):  
Yang Lu ◽  
Bin Cui ◽  
Jiakui Zhao ◽  
Hua Lu ◽  
Jialie Shen
Keyword(s):  
Query Processing ◽  
Real Life ◽  
Road Networks ◽  
Knn Query

Fast kNN query processing over a multi-node GPU environment

2021 ◽  
Author(s):  
Ricardo J. Barrientos ◽  
Javier A. Riquelme ◽  
Ruber Hernández-García ◽  
Cristóbal A. Navarro ◽  
Wladimir Soto-Silva
Keyword(s):  
Query Processing ◽  
Knn Query

scholarly journals kNN Query Processing in Metric Spaces Using GPUs

2011 ◽  
pp. 380-392 ◽  
Author(s):  
Ricardo J. Barrientos ◽  
José I. Gómez ◽  
Christian Tenllado ◽  
Manuel Prieto Matias ◽  
Mauricio Marin
Keyword(s):  
Query Processing ◽  
Metric Spaces ◽  
Knn Query

On authenticated skyline query processing over road networks

2020 ◽  
pp. e5747
Author(s):  
Xiaoyu Zhu ◽  
Jie Wu ◽  
Wei Chang ◽  
Md Zakirul Alam Bhuiyan ◽  
Kim-Kwang Raymond Choo ◽  
...  
Keyword(s):  
Query Processing ◽  
Road Networks ◽  
Skyline Query ◽  
Skyline Query Processing

scholarly journals Efficient Shared Execution Processing of k-Nearest Neighbor Joins in Road Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hyung-Ju Cho
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Real Life ◽  
Road Networks ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Primitive Operation ◽  
Nested Loop

We investigate the k-nearest neighbor (kNN) join in road networks to determine the k-nearest neighbors (NNs) from a dataset S to every object in another dataset R. The kNN join is a primitive operation and is widely used in many data mining applications. However, it is an expensive operation because it combines the kNN query and the join operation, whereas most existing methods assume the use of the Euclidean distance metric. We alternatively consider the problem of processing kNN joins in road networks where the distance between two points is the length of the shortest path connecting them. We propose a shared execution-based approach called the group-nested loop (GNL) method that can efficiently evaluate kNN joins in road networks by exploiting grouping and shared execution. The GNL method can be easily implemented using existing kNN query algorithms. Extensive experiments using several real-life roadmaps confirm the superior performance and effectiveness of the proposed method in a wide range of problem settings.

A pure peer-to-peer approach for kNN query processing in mobile ad hoc networks

2012 ◽  
Vol 17 (5) ◽  
pp. 973-985 ◽  
Author(s):  
Thao P. Nghiem ◽  
Agustinus Borgy Waluyo ◽  
David Taniar
Keyword(s):  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Query Processing ◽  
Ad Hoc ◽  
Peer To Peer ◽  
Knn Query ◽  
Mobile Ad Hoc ◽  
Hoc Networks
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