View field nearest neighbor: A novel type of spatial queries

Yi, Sungmin; Ryu, Hyoseok; Son, Jihoon; Chung, Yon Dohn

doi:10.1016/j.ins.2014.02.022

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…Given the location of the query object and the viewing angle range, the algorithm returns the K-nearest neighbors within the viewing angle range. View-field K-nearest-neighbor (VFkNN) query, which was proposed by Yi et al [20], solves similar problems to direction-aware spatial keyword search; however, and VFkNN supports query processing of moving objects. This method uses a grid to index interest points, accesses the grid within the perspective, and directly excludes the grid outside the perspective, which substantially increases the query efficiency.…”

Section: Direction-based Knn Querymentioning

confidence: 99%

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

Dong

Yuan

Shang

et al. 2019

IJGI

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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.

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Section: Direction-based Knn Querymentioning

confidence: 99%

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

Dong

Yuan

Shang

et al. 2019

IJGI

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“…Numerous algorithms and data structures (e.g., B-tree [26], grid structure [27], or R-tree [28]) have been proposed for kNN query processing. Only a few of them are capable of processing "visible" kNN queries [4,14,15,[18][19][20][21][22][23][24][25]. We can classify these works into two types: (1) visible kNN queries with the obstacle constraint and (2) visible kNN queries with the view field constraint.…”

Section: Related Workmentioning

confidence: 99%

“…Yi et al [23,24] introduced the View Field Nearest Neighbor (VFNN) query that can retrieve the closest visible data object residing in a user's view field. In [23], the authors used R*-tree [32] to index data objects and develop a branch-and-bound search algorithm to traverse the R*-tree to find the results.…”

Section: Visible Knn Queries With the View Field Constraintmentioning

confidence: 99%

Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field †

Chen

Lee

et al. 2019

IJGI

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In many spatial applications, users are only interested in data objects that are visible to them. Hence, finding visible data objects is an important operation in these real-world spatial applications. This study addressed a new type of spatial query, the View field-aware Visible k Nearest Neighbor (V2-kNN) query. Given the location of a user and his/her view field, a V2-kNN query finds data object p so that p is the nearest neighbor of and visible to the user, where visible means the data object is (1) not hidden by obstacles and (2) inside the view field of the user. Previous works on visible NN queries considered only one of these two factors, but not both. To the best of our knowledge, this work is the first to consider both the effect of obstacles and the restriction of the view field in finding the solutions. To support efficient processing of V2-kNN queries, a grid structure is used to index data objects and obstacles. Pruning heuristics are also designed so that only data objects and obstacles relevant to the final query result are accessed. A comprehensive experimental evaluation using both real and synthetic datasets is performed to verify the effectiveness of the proposed algorithms.

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“…The NN problem has been extensively studied during the last decade and is still an attractive topic currently [24,32,46,11,22]. In this section, we briefly review some existing work on NN search in Euclidean space and road networks with certain objects, and PNN search in Euclidean space with uncertain objects.…”

Section: Related Workmentioning

confidence: 99%

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

et al. 2015

Information Sciences

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View field nearest neighbor: A novel type of spatial queries

Cited by 14 publications

References 15 publications

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

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

Finding Visible kNN Objects in the Presence of Obstacles within the User’s View Field †

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

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