Voronoi-based reverse nearest neighbor query processing on spatial networks

Safar, Maytham; Ibrahimi, Dariush; Taniar, David

doi:10.1007/s00530-009-0167-z

Cited by 79 publications

(29 citation statements)

References 29 publications

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“…Any network node located in a Voronoi cell has a shortest path to its corresponding Voronoi generator that is always shorter than that to any other Voronoi generator. A large number of studies adopted network Voronoi diagrams [12] to evaluate variety of proximity queries on road networks (e.g., [7,11,13,27,17]). For example, in [13] Okabe et al introduced six different types of network Voronoi diagrams (each corresponds to very important real-world applications) whose generators are based on points, sets of points, lines and polygons, and whose distances are given by inward/outward distances, and additively/multiplicatively weighted shortest path distances.…”

Section: Introductionmentioning

confidence: 99%

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Indexing Network Voronoi Diagrams

Demiryurek

Shahabi

2012

Database Systems for Advanced Applications

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Abstract. The Network Voronoi diagram and its variants have been extensively used in the context of numerous applications in road networks, particularly to efficiently evaluate various spatial proximity queries such as k nearest neighbor (kNN), reverse kNN, and closest pair. Although the existing approaches successfully utilize the network Voronoi diagram as a way to partition the space for their specific problems, there is little emphasis on how to efficiently find and access the network Voronoi cell containing a particular point or edge of the network. In this paper, we study the index structures on network Voronoi diagrams that enable exact and fast response to contain query in road networks. We show that existing index structures, treating a network Voronoi cell as a simple polygon, may yield inaccurate results due to the network topology, and fail to scale to large networks with numerous Voronoi generators. With our method, termed Voronoi-Quad-tree (or VQ-tree for short), we use Quad-tree to index network Voronoi diagrams to address both of these shortcomings. We demonstrate the efficiency of VQ-tree via experimental evaluations with real-world datasets consisting of a variety of large road networks with numerous data objects.

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Section: Introductionmentioning

confidence: 99%

“…Currently, indexing network Voronoi diagram with R-tree (referred as Voronoi R-tree or VR-tree for short) is the only known method for locating the network Voronoi cell that contains a particular point or edge of the network. VR-tree is first proposed in [7] and later used in many other approaches based on NVD (e.g., [11,27,17]). …”

Section: Introductionmentioning

confidence: 99%

Indexing Network Voronoi Diagrams

Demiryurek

Shahabi

2012

Database Systems for Advanced Applications

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“…Yiu et al [17] first addressed the issue of RNN in road networks (they represented road networks as graphs) and proposed an algorithm for both MRkNN and BRkNN queries. Safar et al [23] presented a framework for RNN queries based on network Voronoi diagrams (NVDs) to efficiently process RNN queries in road networks. However, their scheme is not suitable for continuous RNN queries because NVDs change whenever a dataset changes its location, resulting in high computation costs.…”

Section: Algorithms For Continuous Reverse Nearest Neighbor Query Promentioning

confidence: 99%

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

Attique

Cho

Jin

et al. 2016

IJGI

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A reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k closest points. In recent years, considerable research has been conducted into monitoring reverse k nearest neighbor queries. In this paper, we study the problem of continuous reverse nearest neighbor queries where both the query object q and data objects are moving. Existing state-of-the-art techniques are sensitive towards the movement of data objects, e.g., a candidate object must be verified whenever it changes its location. Further, insufficient attention has been given to the monitoring of RNN queries in dynamic road networks where the network weight changes depending on the traffic conditions. In this paper, we address these problems by proposing a new safe exit-based algorithm called CORE-X for efficiently computing the safe exit points of both query and data objects. The safe exit point of an object indicates the point at which its safe region and non-safe region meet, thus a set of safe exit points represents the border of the safe region. Within the safe region, the query result remains unchanged provided the query and data objects remain inside their respective safe regions. The results of extensive experiments conducted using real road maps indicate that the proposed algorithm significantly reduces communication and computation costs compared to the state-of-the-art algorithm.

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“…Motivated by this, spatial-temporal queries for moving objects have been studied extensively in the fields of moving object databases (MOD) and geographical information systems (GIS) [8][9][10][11]. Many types of queries and corresponding efficient query algorithms have been proposed, such as range queries [12,13], k-nearest neighbor queries [14][15][16], reverse nearest neighbor queries [17], skyline queries [18], density queries [19,20] and visible nearest neighbor queries [21].…”

Section: Introductionmentioning

confidence: 99%

“…However, it has a strong dependence on the design of in-memory data structures to maintain the reusable candidates, such as an expansion tree and a safe region [13,25,32]. However, when the scale of networks is large, but the distribution of moving objects is relatively sparse, these data structures require more online re-computation to maintain the up-to-date query candidates, because the objects frequently enter and leave the regions of the expansion tree [16,17]. That invalidates these data structures and results in performance degradation.…”

Section: Introductionmentioning

confidence: 99%

A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

Zhang

Lü

Chen

2016

IJGI

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Abstract:The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR) query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET) structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency.

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Voronoi-based reverse nearest neighbor query processing on spatial networks

Cited by 79 publications

References 29 publications

Indexing Network Voronoi Diagrams

Indexing Network Voronoi Diagrams

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

A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

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