Definition
Given two sets P and Q of objects, a closest pair (CP) query discovers the pair of objects (p, q) with a distance that is the smallest among all object pairs in the Cartesian product P×Q. Similarly, a k closest pair query (k-CPQ) retrieves k pairs of objects from P and Q with the minimum distances among all the object pairs. In spatial databases, the distance is usually defined according to the Euclidean metric, and the set of objects P and Q are disk-resident. Query algorithms aim at minimizing the processing cost and the number of I/O operations, by using several optimization techniques for pruning the search space.
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Beckmann N., Kriegel H.P., Schneider R., and Seeger B. The R*-tree: an efficient and robust access method for points and rectangles. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1990, pp. 322–331.
Böhm C. and Krebs F. The k-nearest neighbour join: Turbo charging the KDD process. Knowl. Inform. Syst., 6(6):728–749, 2004.
Chan E.P.F. Buffer queries. IEEE Trans. Knowl. Data Eng., 15(4):895–910, 2003.
Corral A., Manolopoulos Y., Theodoridis Y., and Vassilakopoulos M. Closest pair queries in spatial databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 189–200.
Corral A., Manolopoulos Y., Theodoridis Y., and Vassilakopoulos M. Algorithms for processing K-closest-pair queries in spatial databases. Data Knowl. Eng., 49(1):67–104, 2004.
Corral A., Manolopoulos Y., Theodoridis Y., and Vassilakopoulos M. Multi-way distance join queries in spatial databases. GeoInformatica, 8(4):373–402, 2004.
Corral A., Manolopoulos Y., Theodoridis Y., and Vassilakopoulos M. Cost models for distance joins queries using R-trees. Data Knowl. Eng., 57(1):1–36, 2006.
Hjaltason G.R. and Samet H. Incremental distance join algorithms for spatial databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 237–248.
Iwerks G.S., Samet H., and Smith K. Maintenance of spatial semijoin queries on moving points. In Proc. 30th Int. Conf. on Very Large Data Bases, 2004, pp. 828–839.
Nanopoulos A., Theodoridis Y., and Manolopoulos Y. C2P: clustering based on closest pairs. In Proc. 27th Int. Conf. on Very Large Data Bases, 2001, pp. 331–340.
Papadopoulos A.N., Nanopoulos A., and Manolopoulos Y. Processing distance join queries with constraints. Comput. J., 49(3):281–296, 2006.
Shin H., Moon B., and Lee S. Adaptive multi-stage distance join processing. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 343–354.
Shou Y., Mamoulis N., Cao H., Papadias D., and Cheung D.W. Evaluation of iceberg distance joins. In Proc. 8th Int. Symp. Advances in Spatial and Temporal Databases, 2003, pp. 270–288.
Yang C. and Lin K. An index structure for improving closest pairs and related join queries in spatial databases. In Proc. Int. Conf. on Database Eng. and Applications, 2002, pp. 140–149.
Zhu M., Lee D.L., and Zhang J. k-closest pair query monitoring over moving objects. In Proc. 3rd Int. Conf. on Mobile Data Management, 2002, pp. 14–14.
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Corral, A., Vassilakopoulos, M. (2009). Closest-Pair Query. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_67
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DOI: https://doi.org/10.1007/978-0-387-39940-9_67
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