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Processing Distance Join Queries with Constraints

Processing Distance Join Queries with Constraints Distance join queries are used in many modern applications, such as spatial databases, spatiotemporal databases and data mining. One of the most common distance join queries is the closest-pair query (CPQ). Given two datasets DAand DB the CPQ retrieves the pair (a, b), where a ∈ DA and b ∈ DB, having the smallest distance between all pairs of objects. An extension to this problem is to generate the k closest pairs of objects (k-CPQ). In several cases spatial constraints are applied, and object pairs that are retrieved must also satisfy these constraints. Although the application of spatial constraints seems natural towards a more focused search, only recently they have been studied for the CPQ problem with the restriction that DA = DB. In this work, we focus on constrained closest-pair queries, between two distinct datasets DA and DB, where objects from DA must be enclosed by a spatial region R. Several algorithms are presented and evaluated using real-life and synthetic datasets. Among them, a heap-based method enhanced with batch capabilities outperforms the other approaches as it is demonstrated by an extensive performance evaluation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Computer Journal Oxford University Press

Processing Distance Join Queries with Constraints

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References (23)

Publisher
Oxford University Press
Copyright
© Published by Oxford University Press.
ISSN
0010-4620
eISSN
1460-2067
DOI
10.1093/comjnl/bxl002
Publisher site
See Article on Publisher Site

Abstract

Distance join queries are used in many modern applications, such as spatial databases, spatiotemporal databases and data mining. One of the most common distance join queries is the closest-pair query (CPQ). Given two datasets DAand DB the CPQ retrieves the pair (a, b), where a ∈ DA and b ∈ DB, having the smallest distance between all pairs of objects. An extension to this problem is to generate the k closest pairs of objects (k-CPQ). In several cases spatial constraints are applied, and object pairs that are retrieved must also satisfy these constraints. Although the application of spatial constraints seems natural towards a more focused search, only recently they have been studied for the CPQ problem with the restriction that DA = DB. In this work, we focus on constrained closest-pair queries, between two distinct datasets DA and DB, where objects from DA must be enclosed by a spatial region R. Several algorithms are presented and evaluated using real-life and synthetic datasets. Among them, a heap-based method enhanced with batch capabilities outperforms the other approaches as it is demonstrated by an extensive performance evaluation.

Journal

The Computer Journal – Oxford University Press

Published: Mar 3, 2006

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