Node and edge selectivity estimation for range queries in spatial networks

Tiakas, Eleftherios; Papadopoulos, Apostolos N.; Νανόπουλος, Αλέξανδρος; Manolopoulos, Y.

doi:10.1016/j.is.2008.09.004

Cited by 4 publications

(6 citation statements)

References 19 publications

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“…Several methods were proposed in [41]. However, they are only able to estimate the number of nodes and edges in the range.…”

Section: Selectivity Estimation Of Rsas Queriesmentioning

confidence: 99%

“…As discussed in Section 4.3, Selectivity estimation of range queries on road networks is a much harder problem than its counterpart in the euclidean space. Several methods for selectivity estimation for range queries on road networks were proposed in [41]. However, they are only able to estimate the number of nodes and edges in the range (not the number of points residing on the network in the range).…”

Section: Related Workmentioning

confidence: 99%

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Spatial Approximate String Search

Yao

Tang

et al. 2013

IEEE Trans. Knowl. Data Eng.

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This work deals with the approximate string search in large spatial databases. Specifically, we investigate range queries augmented with a string similarity search predicate in both euclidean space and road networks. We dub this query the spatial approximate string (SAS) query. In euclidean space, we propose an approximate solution, the MHR-tree, which embeds min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings under the subtree of u. We analyze the pruning functionality of such signatures based on the set resemblance between the query string and the q-grams from the subtrees of index nodes. We also discuss how to estimate the selectivity of a SAS query in euclidean space, for which we present a novel adaptive algorithm to find balanced partitions using both the spatial and string information stored in the tree. For queries on road networks, we propose a novel exact method, RSASSOL, which significantly outperforms the baseline algorithm in practice. The RSASSOL combines the q-gram-based inverted lists and the reference nodes based pruning. Extensive experiments on large real data sets demonstrate the efficiency and effectiveness of our approaches.Index Terms-Approximate string search, range query, road network, spatial databases 1394

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“…Several methods were proposed in [41]. However, they are only able to estimate the number of nodes and edges in the range.…”

Section: Selectivity Estimation Of Rsas Queriesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spatial Approximate String Search

Yao

Tang

et al. 2013

IEEE Trans. Knowl. Data Eng.

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“…These numbers essentially determine the time complexity of our influence zone-based approach. Tiakas et al [31] provide a comprehensive study to estimate the number of nodes and edges covered by a range query of range r . Let L e denote the average edge length of a graph.…”

Section: Number Of Accessed Nodes and Edgesmentioning

confidence: 99%

“…Let L e denote the average edge length of a graph. Given a spatial network that can be modeled by an 12 S. Wang et al infinite regular uniform 2 graph [31], the number of nodes N R covered by such a range query and the number of edges E R covered by such a range query are given by the following equations,respectively:…”

Section: Number Of Accessed Nodes and Edgesmentioning

confidence: 99%

Efficiently Monitoring Reverse k-Nearest Neighbors in Spatial Networks

Wang

Cheema

Lin

2013

The Computer Journal

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Given a set of clients C, a set of facilities F and a query q ∈ F, a reverse k-nearest neighbor (RkNN) query retrieves every client c ∈ C for which q is one of the k closest facilities. In the past few years, RkNN queries have received significant research attention due to their wide range of applications. In this paper, we study the problem of continuous monitoring of RkNN queries in road networks. The state-of-the-art technique is sensitive toward the movement of clients, e.g. whenever a client that is inside the so-called unpruned region changes its location, the existing technique requires expensive verification of whether the client is an RkNN of q or not. To address this problem, we utilize the novel concept of influence zone, which is a region in the network such that a client c is the RkNN if and only if it lies inside this zone. This significantly improves performance because the problem of continuously monitoring RkNN queries is reduced to the problem of continuously monitoring the clients that are inside this zone. Based on several non-trivial observations, we present an efficient algorithm to compute the influence zone. Our extensive experimental study demonstrates that our algorithm is more than an order of magnitude faster than the state-of-the-art algorithm.

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“…With spatial networks, E.Tiakas et al present methods to estimate the number of vertices and edges that are lying within a certain distance from a vertex [13], [14].…”

Section: Preliminary and Related Workmentioning

confidence: 99%

Spatial distance join query estimation without data access

Xiong

Jing

et al. 2011

2011 IEEE International Conference on Computer Science and Automation Engineering

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Spatial join query is one of the fundamental operations in spatial database systems and geographic information systems. Existing estimating methods often make uniformity assumption, which seldom holds in realistic situation. Moreover, little attention has been paid to distance join, instead of intersection join. This paper first develops a MBR-buffer based method, which can be used to get satisfying estimation results with only several statistics. Then, to improve relative errors, an enhanced method using line segment distribution law for distance join among different kinds of datasets is proposed. Without scanning the entire dataset, this method can provide accurate estimating results. Experiments show that our techniques are more efficient for estimating the selectivity of distance join, and more applicable to realistic datasets.___________________________________ 978-1-4244-8728-8/11/$26.00 ©2011 IEEE

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Node and edge selectivity estimation for range queries in spatial networks

Cited by 4 publications

References 19 publications

Spatial Approximate String Search

Spatial Approximate String Search

Efficiently Monitoring Reverse k-Nearest Neighbors in Spatial Networks

Spatial distance join query estimation without data access

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