A collaborative approach to moving k-nearest neighbor queries in directed and dynamic road networks

2015 ◽  
Vol 17 ◽  
pp. 139-156 ◽  
Author(s):  
Hyung-Ju Cho ◽  
Rize Jin ◽  
Tae-Sun Chung
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Collaborative Approach ◽  
Nearest Neighbor Queries ◽  
Dynamic Road Networks

scholarly journals Efficient Processing of All Nearest Neighbor Queries in Dynamic Road Networks

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1137
Author(s):  
Aavash Bhandari ◽  
Aziz Hasanov ◽  
Muhammad Attique ◽  
Hyung-Ju Cho ◽  
Tae-Sun Chung
Keyword(s):  
Road Network ◽  
Nearest Neighbor ◽  
Computational Cost ◽  
Road Networks ◽  
Coherence Property ◽  
Traffic Conditions ◽  
Efficient Processing ◽  
Data Objects ◽  
Nearest Neighbor Queries ◽  
Dynamic Road Networks

The increasing trend of GPS-enabled smartphones has led to the tremendous usage of Location-Based Service applications. In the past few years, a significant amount of studies have been conducted to process All nearest neighbor (ANN) queries. An ANN query on a road network extracts and returns all the closest data objects for all query objects. Most of the existing studies on ANN queries are performed either in Euclidean space or static road networks. Moreover, combining the nearest neighbor query and join operation is an expensive procedure because it requires computing the distance between each pair of query objects and data objects. This study considers the problem of processing the ANN queries on a dynamic road network where the weight, i.e., the traveling distance and time varies due to various traffic conditions. To address this problem, a shared execution-based approach called standard clustered loop (SCL) is proposed that allows efficient processing of ANN queries on a dynamic road network. The key concept behind the shared execution technique is to exploit the coherence property of road networks by clustering objects that share common paths and processing the cluster as a single path. In an empirical study, the SCL method achieves significantly better performance than competitive methods and efficiently reduces the computational cost to process ANN queries in various problem settings.

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

Aggregate keyword nearest neighbor queries on road networks

2017 ◽  
Vol 22 (2) ◽  
pp. 237-268 ◽  
Author(s):  
Pengfei Zhang ◽  
Huaizhong Lin ◽  
Yunjun Gao ◽  
Dongming Lu
Keyword(s):  
Nearest Neighbor ◽  
Road Networks ◽  
Nearest Neighbor Queries

Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks

2012 ◽  
Vol 37 (1) ◽  
pp. 13-32 ◽  
Author(s):  
Ping Fan ◽  
Guohui Li ◽  
Ling Yuan ◽  
Yanhong Li
Keyword(s):  
Moving Objects ◽  
Nearest Neighbor ◽  
Road Networks ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Queries
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