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
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 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
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
High Dimensional Similarity Joins: Algorithms and Performance Evaluation
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Database Systems Concepts
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
On computing top-t most influential spatial sites
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Multidimensional reverse kNN search
The VLDB Journal — The International Journal on Very Large Data Bases
Gorder: an efficient method for KNN join processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Common Influence Join: A Natural Join Operation for Spatial Pointsets
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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We introduce a novel spatial join operator, the ring-constrained join (RCJ). Given two sets P and Q of spatial points, the result of RCJ consists of pairs (p, q) (where p ε P, q ε Q) satisfying an intuitive geometric constraint: the smallest circle enclosing p and q contains no other points in P, Q. This new operation has important applications in decision support, e.g., placing recycling stations at fair locations between restaurants and residential complexes. Clearly, RCJ is defined based on a geometric constraint but not on distances between points. Thus, our operation is fundamentally different from the conventional distance joins and closest pairs problems. We are not aware of efficient processing algorithms for RCJ in the literature. A brute-force solution requires computational cost quadratic to input size and it does not scale well for large datasets. In view of this, we develop efficient R-tree based algorithms for computing RCJ, by exploiting the characteristics of the geometric constraint. We evaluate experimentally the efficiency of our methods on synthetic and real spatial datasets. The results show that our proposed algorithms scale well with the data size and have robust performance across different data distributions.