Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Multi step Query Processing

  • Peer Kröger
  • Matthias Renz
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_227

Synonyms

Filter/refinement query processing

Definition

A query on a database reports those objects which fulfill a given query predicate. A query processor has to evaluate the query predicate for each object in the database which is a candidate for the result set. Multi-step query processing (filter/refinement query processing) is a technique to speed up queries specifying query predicates that are complex and costly to evaluate. The idea is to save the costs of the evaluation of the complex query predicate by reducing the candidate set for which the query predicate has to be evaluated applying one or more filter steps. The aim of each filter step is to identify as many true hits (objects that truly fulfill the complex query predicate) and as many true drops (objects that truly do not fulfill the query predicate) as possible by applying a less costly query predicate. The remaining candidates that are not pruned as drops or reported as hits in one of the filter steps need to be tested...

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Recommended Reading

  1. 1.
    Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases. In: Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms; 1993. p. 69–80.CrossRefGoogle Scholar
  2. 2.
    Brinkhoff T, Horn H, Kriegel H-P, Schneider R. A storage and access architecture for efficient query processing in spatial database systems. In: Proceedings of the 3rd International Symposium on Advances in Spatial Databases; 1993. p. 357–76.CrossRefGoogle Scholar
  3. 3.
    Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time series database. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1994. p. 419.CrossRefGoogle Scholar
  4. 4.
    Korn F, Sidiropoulos N, Faloutsos C, Siegel E, Protopapas Z. Fast nearest neighbor search in medical image databases. In: Proceedings of the 22nd International Conference on Very Large Data Bases; 1996.p. 215–26.Google Scholar
  5. 5.
    Kriegel H-P, Kröger P, Kunath P, Renz M. Generalizing the optimality of multi-step k-nearest neighbor query processing. In: Proceedings of the 10th International Symposium on Advances in Spatial and Temporal Databases; 2007. p. 75–9.Google Scholar
  6. 6.
    Orenstein J, Manola F. Probe spatial data modelling and query processing in an image database application. IEEE Trans Softw Eng. 1998;14(5):611–29.CrossRefGoogle Scholar
  7. 7.
    Seidl T, Kriegel H-P. Optimal multi-step k-nearest neighbor search. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 154–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ludwig-Maximilians-UniversitätMünchenGermany

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Dept. of Computer Science and Eng.Hong Kong Univ. of Science and TechnologyKowloonHong Kong SAR