The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Join processing in relational databases
ACM Computing Surveys (CSUR)
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adaptive multi-stage distance join processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast hierarchical clustering and other applications of dynamic closest pairs
Journal of Experimental Algorithmics (JEA)
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
High-Dimensional Similarity Joins
IEEE Transactions on Knowledge and Data Engineering
C2P: Clustering based on Closest Pairs
Proceedings of the 27th International Conference on Very Large Data Bases
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
All-Nearest-Neighbors Queries in Spatial Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
The k-Nearest Neighbour Join: Turbo Charging the KDD Process
Knowledge and Information Systems
Gorder: an efficient method for KNN join processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Review of data consistency and integrity constraints in spatial databases
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
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Given two datasets $\mathcal{D}_{A}$ and $\mathcal{D}_{B}$ the closest-pair query (CPQ) retrieves the pair (a,b), where $a \epsilon \mathcal{D}_{A}$ and $b \epsilon \mathcal{D}_{B}$, 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 $\mathcal{D}_{A}$ = $\mathcal{D}_{B}$. In this work we focus on constrained closest-pair queries (CCPQ), between two distinct datasets $\mathcal{D}_{A}$ and $\mathcal{D}_{B}$, where objects from $\mathcal{D}_{A}$ must be enclosed by a spatial region R. A new algorithm is proposed, which is compared with a modified closest-pair algorithm. The experimental results demonstrate that the proposed approach is superior with respect to CPU and I/O costs.