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. Given two datasets DA and DB the closest-pair query (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 (CCPQ), 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.