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.

scholarly journals Efficient Processing of Moving Top-k Spatial Keyword Queries in Directed and Dynamic Road Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Attique ◽  
Hyung-Ju Cho ◽  
Tae-Sun Chung
Keyword(s):  
Euclidean Space ◽  
Query Processing ◽  
Processing Time ◽  
Road Networks ◽  
Spatial Networks ◽  
Weight Changes ◽  
Traffic Conditions ◽  
Data Object ◽  
Efficient Processing ◽  
Dynamic Road Networks

A top-k spatial keyword (TkSk) query ranks objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top-k spatial keyword queries. However, most of the studies focus on Euclidean space or only investigate the snapshot queries where both the query and data object are static. A few algorithms study TkSk queries in undirected road networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, TkSk queries have not been thoroughly investigated in directed and dynamic spatial networks where each edge has a particular orientation and its weight changes according to the traffic conditions. Therefore, in this study, we address this problem by presenting a new method, called COSK, for processing continuous top-k spatial keyword queries for moving queries in directed and dynamic road networks. We first propose an efficient framework to process snapshot TkSK queries. Furthermore, we propose a safe-exit-based approach to monitor the validity of the results for moving TkSK queries. Our experimental results demonstrate that COSK significantly outperforms existing techniques in terms of query processing time and communication cost.

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

scholarly journals α-Probabilistic flexible aggregate nearest neighbor search in road networks using landmark multidimensional scaling

2020 ◽  
Author(s):  
Moonyoung Chung ◽  
Woong-Kee Loh
Keyword(s):  
Multidimensional Scaling ◽  
Shortest Path ◽  
Road Network ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Road Networks ◽  
Path Distance ◽  
Aggregate Nearest Neighbor ◽  
Shortest Path Distance

AbstractIn spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects $$Q = \{ q_0, \dots , q_{M-1} \}$$ Q = { q 0 , ⋯ , q M - 1 } and finds the object $$p^*$$ p ∗ that minimizes $$g \{ d(p^*, q_i), q_i \in Q \}$$ g { d ( p ∗ , q i ) , q i ∈ Q } , where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor $$\phi \, (0 < \phi \le 1)$$ ϕ ( 0 < ϕ ≤ 1 ) and finds the object $$p^*$$ p ∗ and the set of query objects $$Q^*_\phi $$ Q ϕ ∗ that minimize $$g \{ d(p^*, q_i), q_i \in Q^*_\phi \}$$ g { d ( p ∗ , q i ) , q i ∈ Q ϕ ∗ } , where $$Q^*_\phi $$ Q ϕ ∗ can be any subset of Q of size $$\phi |Q|$$ ϕ | Q | . This study proposes an efficient $$\alpha $$ α -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-$$k\hbox {NN}$$ k NN , used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains $$p^*$$ p ∗ . However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-$$k\hbox {NN}$$ k NN looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-$$k\hbox {NN}$$ k NN ; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.

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