Nearest neighbor queries in road networks

Jensen, Christian S.; Kolářvr, Jan; Pedersen, Torben Bach; Timko, Igor

doi:10.1145/956676.956677

Cited by 166 publications

(98 citation statements)

References 22 publications

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“…Previous studies (e.g., nearest neighbor query [10,9,11,23]) only use spatial distance as the sole factor when computing the query results. In contrast, the RD query takes both spatial distance and density distribution into account.…”

Section: Introductionmentioning

confidence: 99%

“…The distance between a region and a query point is defined by the shortest network distance between the region center point and the query point (e.g., dist(ω 2 , q) = dist(p 2 , q)). If considering the spatial distance only (e.g., the same as the nearest neighbor query [9]), ω 2 is the region closest to q. However, when considering both spatial distance and the density of spatial objects, ω 2 is less attractive than ω 3 because of its sparser spatial-object distribution.…”

Section: Introductionmentioning

confidence: 99%

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Finding regions of interest using location based social media

et al. 2016

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The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding regions of interest using location based social media

et al. 2016

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“…An important class of location based queries consists of proximity queries such as k Nearest Neighbor(kNN) query [15,32,21,6,7] and its variations, e.g., Reverse k Nearest Neighbor (RkNN) [23,29], k Aggregate Nearest Neighbor (kANN) [28]. The proximity queries in general search for data objects that minimize a distance-based function with reference to one or more query objects.…”

Section: Introductionmentioning

confidence: 99%

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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“…Recently, the research focus is brought to spatial network databases (SNDB) where objects are restricted to move on predefined roads [11,6,9,8]. In SNDB, a road network is modeled as a graph G (< V, E >), where a vertex (node) denotes a road junction and an edge denotes the road between two junctions; and the weight of the edge denotes the network distance.…”

Section: Introductionmentioning

confidence: 99%

“…In the first category, NN search expands from the query node to adjacent nodes until a data object is found and further expansion cannot retrieve closer objects [6,9]. Such network expansion originates from Dijkstra's algorithm that finds single-source shortest paths.…”

Section: Introductionmentioning

confidence: 99%

Fast Nearest Neighbor Search on Road Networks

Lee

2006

Lecture Notes in Computer Science

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Abstract. Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstra-like network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (V N 3 ) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected tree-based structures called SPIE's. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions.

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Nearest neighbor queries in road networks

Cited by 166 publications

References 22 publications

Finding regions of interest using location based social media

Finding regions of interest using location based social media

Indexing Network Voronoi Diagrams

Fast Nearest Neighbor Search on Road Networks

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