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Closest-Pair Query

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Synonyms

Closest pairs; Incremental k-distance join; k-Closest pair join; k-Closest pair query; k-Distance join

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.

Historical Background

The closest pair query, has been widely studied in computational geometry. More recently, this problem has been approached in the context of spatial databases [4, 8, 12, 14]. In spatial databases, existing algorithms assume...

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Recommended Reading

  1. Beckmann N, Kriegel HP, Schneider R, Seeger B. The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1990. p. 322–31.

    Google Scholar 

  2. Böhm C, Krebs F. The k-nearest neighbour join: turbo charging the KDD process. Knowl Inform Syst. 2004;6(6):728–49.

    Article  Google Scholar 

  3. Chan EPF. Buffer queries. IEEE Trans Knowl Data Eng. 2003;15(4):895–910.

    Article  Google Scholar 

  4. Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Closest pair queries in spatial databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 189–200.

    Article  Google Scholar 

  5. Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Algorithms for processing K-closest-pair queries in spatial databases. Data Knowl Eng. 2004;49(1):67–104.

    Article  Google Scholar 

  6. Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Multi-way distance join queries in spatial databases. GeoInformatica. 2004;8(4):373–402.

    Article  Google Scholar 

  7. Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Cost models for distance joins queries using R-trees. Data Knowl Eng. 2006;57(1):1–36.

    Article  Google Scholar 

  8. Hjaltason GR, Samet H. Incremental distance join algorithms for spatial databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 237–48.

    Article  Google Scholar 

  9. Iwerks GS, Samet H, Smith K. Maintenance of spatial semijoin queries on moving points. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 828–39.

    Chapter  Google Scholar 

  10. Nanopoulos A, Theodoridis Y, Manolopoulos Y. C2P: clustering based on closest pairs. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 331–40.

    Google Scholar 

  11. Papadopoulos AN, Nanopoulos A, Manolopoulos Y. Processing distance join queries with constraints. Comput J. 2006;49(3):281–96.

    Article  Google Scholar 

  12. Shin H, Moon B, Lee S. Adaptive multi-stage distance join processing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 343–54.

    Article  Google Scholar 

  13. Shou Y, Mamoulis N, Cao H, Papadias D, Cheung D.W. Evaluation of iceberg distance joins. In: Proceedings of the 8th International Symposium Advances in Spatial and Temporal Databases; 2003. p.~270–88.

    Chapter  Google Scholar 

  14. Yang C, Lin K. An index structure for improving closest pairs and related join queries in spatial databases. In: Proceedings of the International Conference on Database Engineering and Applications; 2002. p. 140–49.

    Google Scholar 

  15. Zhu M, Lee DL, Zhang J. k-closest pair query monitoring over moving objects. In: Proceedings of the 3rd International Conference on Mobile Data Management; 2002. p. 14–14.

    Google Scholar 

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Correspondence to Antonio Corral .

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Corral, A., Vassilakopoulos, M. (2018). Closest-Pair Query. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_67

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