The Computer Journal Advance Access originally published online on March 3, 2006
The Computer Journal 2006 49(3):281-296; doi:10.1093/comjnl/bxl002
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Processing Distance Join Queries with Constraints
Data Engineering Research Laboratory, Department of Informatics, Aristotle University Thessaloniki 54124, Greece
*Corresponding author: apostol{at}delab.csd.auth.gr
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 
and 
the CPQ retrieves the pair (a, b), where a 
and b , 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 = . In this work, we focus on constrained closest-pair queries, between two distinct datasets and , where objects from 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.
Key Words: Spatial data • closest-pair queries • spatial constraints