Query Processing in Spatial Network Databases

Papadias, Dimitris; Zhang, Jun; Mamoulis, Nikos; Tao, Yufei

doi:10.1016/b978-012722442-8/50076-8

Cited by 535 publications

(586 citation statements)

References 20 publications

(17 reference statements)

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“…Actually, any efficient SP -query algorithm can be utilized in the refinement process of DSG-query, which is orthogonal to our pruning strategies. There are a lot of work on spatial networks [15,14]. Generally speaking, these methods always utilize some spatial properties for processing.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Skyline Queries in Large Graphs

Zou

Chen²,

Özsu

et al. 2010

Database Systems for Advanced Applications

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Abstract. Given a set of query points, a dynamic skyline query reports all data points that are not dominated by other data points according to the distances between data points and query points. In this paper, we study dynamic skyline queries in a large graph (DSG-query for short). Although dynamic skylines have been studied in Euclidean space [16], road network [6], and metric space [4,7], there is no previous work on dynamic skylines over large graphs. We employ a filter-and-refine framework to speed up the query processing that can answer DSG-query efficiently. We propose a novel pruning rule based on graph properties to derive the candidates for DSG-query, that are guaranteed not to introduce false negatives. In the refinement step, with a carefully-designed index structure, we compute short path distances between vertices in O(H), where H is the number of maximal hops between any two vertices. Extensive experiments demonstrate that our methods outperform existing algorithms by orders of magnitude.

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Section: Related Workmentioning

confidence: 99%

Dynamic Skyline Queries in Large Graphs

Zou

Chen²,

Özsu

et al. 2010

Database Systems for Advanced Applications

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“…With their approach, they indexed the Voronoi cells with R-tree (i.e., VR-tree) to reduce the contain(q) query to a point location problem in the Euclidean space. In [14], Papadias et al introduced Incremental Network Expansion (INE) and Incremental Euclidean Restriction (IER) methods to support kNN queries in spatial networks. While IN E is an adaption of the Dijkstra algorithm, IER exploits the Euclidean restriction principle in which the results are first computed in Euclidean space and then refined by using the network distance.…”

Section: Related Workmentioning

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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“…Shahabi et al [5] introduce an embedding technique to transfer the road network to a constraint-free high dimensional space. Papadias et al [6] introduce techniques for network kNN queries by integrating network and Euclidean information and capturing pragmatic constraints. Kolahdouzan et al [7] propose a Voronoibased algorithm, VN 3 , for spatial network databases.…”

Section: Knn Query Processing Techniquesmentioning

confidence: 99%

Indexing land surface for efficient kNN query

2008

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The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applications heavily operate on this third dimension, i.e., land surface. Hence, for the field of databases to stay relevant, it should be able to efficiently process spatial queries given this constrained third dimension. However, online processing of the surface k Nearest Neighbor (skNN) queries is quite challenging due to the huge size of land surface models which renders any accurate distance computation on the surface extremely slow. In this paper, for the first time, we propose an index structure on land surface that enables exact and fast responses to skNN queries. Two complementary indexing schemes, namely Tight Surface Index (TSI) and Loose Surface Index (LSI), are constructed and stored collectively on a single novel data structure called Surface Index R-tree (SIR-tree). With those indexes, we can process skNN query efficiently by localizing the search and minimizing the invocation of the costly surface distance computation and hence incurring low I/O and computation costs. Our algorithm does not need to know the value of k a priori and can incrementally expand the search region using SIR-tree and report the query result progressively. It also reports the exact shortest surface paths to the query results. We show through experiments with real world data sets that our algorithm has better performance than the competitors in both efficiency and accuracy.

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Query Processing in Spatial Network Databases

Cited by 535 publications

References 20 publications

Dynamic Skyline Queries in Large Graphs

Dynamic Skyline Queries in Large Graphs

Indexing Network Voronoi Diagrams

Indexing land surface for efficient kNN query

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