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Closest-Pair Query

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

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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 that P...

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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 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 ACM SIGMOD International Conference on Management of Data; 2000. p. 189–200.

    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 ACM SIGMOD International Conference on Management of Data; 1998. p. 237–48.

    Google Scholar 

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

    Google Scholar 

  10. Nanopoulos A, Theodoridis Y, Manolopoulos Y. C2P: clustering based on closest pairs. In: Proceedings of 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 ACM SIGMOD International Conference on Management of Data; 2000. p. 343–54.

    Google Scholar 

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

    Google Scholar 

  14. Yang C, Lin K. An index structure for improving closest pairs and related join queries in spatial databases. In: Proceedings of 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 3rd International Conference on Mobile Data Management; 2002. p. 14–14.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Almeria, Almeria, Spain

    Antonio Corral

  2. University of Central Greece, Lamia, Greece

    Michael Vassilakopoulos

Authors
  1. Antonio Corral
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  2. Michael Vassilakopoulos
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Corresponding author

Correspondence to Antonio Corral .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science and Eng., Hong Kong Univ. of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Hong Kong SAR

    Dimitris Papadias

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© 2014 Springer Science+Business Media New York

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Corral, A., Vassilakopoulos, M. (2014). Closest-Pair Query. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_67-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_67-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

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