scholarly journals Efficient Processing of Continuous Reverse k Nearest Neighbor on Moving Objects in Road Networks

2016 ◽  
Vol 5 (12) ◽  
pp. 247 ◽  
Author(s):  
Muhammad Attique ◽  
Hyung-Ju Cho ◽  
Rize Jin ◽  
Tae-Sun Chung
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
K Nearest Neighbor ◽  
Efficient Processing

Continuous reverse k nearest neighbor monitoring on moving objects in road networks

2010 ◽  
Vol 35 (8) ◽  
pp. 860-883 ◽  
Author(s):  
Li Guohui ◽  
Li Yanhong ◽  
Li Jianjun ◽  
LihChyun Shu ◽  
Yang Fumin
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
K Nearest Neighbor

scholarly journals Direction-Aware Continuous Moving K-Nearest-Neighbor Query in Road Networks

2019 ◽  
Vol 8 (9) ◽  
pp. 379 ◽  
Author(s):  
Dong ◽  
Yuan ◽  
Shang ◽  
Ye ◽  
Zhang
Keyword(s):  
Los Angeles ◽  
Data Streams ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Test Results ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Moving Direction ◽  
Query Algorithm

Continuous K-nearest neighbor (CKNN) queries on moving objects retrieve the K-nearest neighbors of all points along a query trajectory. They mainly deal with the moving objects that are nearest to the moving user within a specified period of time. The existing methods of CKNN queries often recommend K objects to users based on distance, but they do not consider the moving directions of objects in a road network. Although a few CKNN query methods consider the movement directions of moving objects in Euclidean space, no efficient direction determination algorithm has been applied to CKNN queries over data streams in spatial road networks until now. In order to find the top K-nearest objects move towards the query object within a period of time, this paper presents a novel algorithm of direction-aware continuous moving K-nearest neighbor (DACKNN) queries in road networks. In this method, the objects’ azimuth information is adopted to determine the moving direction, ensuring the moving objects in the result set towards the query object. In addition, we evaluate the DACKNN query algorithm via comprehensive tests on the Los Angeles network TIGER/LINE data and compare DACKNN with other existing algorithms. The comparative test results demonstrate that our algorithm can perform the direction-aware CKNN query accurately and efficiently.

Continuous K Nearest Neighbor Queries of Moving Objects in Road Networks

2010 ◽  
Vol 33 (8) ◽  
pp. 1396-1404 ◽  
Author(s):  
Liang ZHAO ◽  
Luo CHEN ◽  
Ning JING ◽  
Wei LIAO
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries

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.

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