Encyclopedia of Database Systems

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

Spatial Indexing Techniques

  • Yannis Manolopoulos
  • Yannis Theodoridis
  • Vassilis J. Tsotras
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_355

Synonyms

Spatial access methods

Definition

A spatial index is a data structure designed to enable fast access to spatial data. Spatial data come in various forms, the most common being points, lines, and regions in n-dimensional space (practically, n = 2 or 3 in geographical information system (GIS) applications). Typical “selection” queries include the spatial range query (“find all objects that lie within a given query region”) and the spatial point query (“find all objects that contain a given query point”). In addition, multidimensional data introduce spatial relationships (such as overlapping and disjointness) and operators (e.g., nearest neighbor), which need to be efficiently supported as well. Example queries are the spatial join query (“find all pairs of objects that intersect each other”) and the nearest neighbor query (“find the five objects nearest to a given query point”). It should be noted that traditional indexing approaches (B+-trees, hashing, etc.) are not...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Beckmann N, Kriegel H-P, 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. 2.
    Finkel RA, Bentley JL. Quad Trees: a data structure for retrieval on composite keys. Acta Informatica. 1974;4(1):1–9.zbMATHCrossRefGoogle Scholar
  3. 3.
    Freeston MA. General solution of the n-dimensional B-tree problem. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1995. p. 80–91.CrossRefGoogle Scholar
  4. 4.
    Gaede V, Guenther O. Multidimensional access methods. ACM Comput Surv. 1998;30(2):170–231.CrossRefGoogle Scholar
  5. 5.
    Guttman A. R-trees: a dynamic index structure for spatial searching. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1984. p. 47–57.Google Scholar
  6. 6.
    Hadjieleftheriou M, Hoel E, Tsotras VJ. SaIL: a spatial index library for efficient application integration. GeoInformatica. 2005;9(4):367–89.CrossRefGoogle Scholar
  7. 7.
    Henrich A, Six H-W, Widmayer P. The LSD tree: spatial access to multidimensional point and non point objects. In: Proceedings of the 15th International Conference on Very Large Data Bases; 1989. p. 43–53.Google Scholar
  8. 8.
    Kamel I, Faloutsos C. Hilbert R-tree: an improved R-tree using fractals. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 500–09.Google Scholar
  9. 9.
    Lomet DB, Salzberg B. The hB-tree: a multiattribute indexing method with good guaranteed performance. ACM Trans Database Syst. 1990;15(4): 625–58.CrossRefGoogle Scholar
  10. 10.
    Nievergelt J, Hinterberger H, Sevcik KC. The grid file: an adaptable symmetric multikey file structure. ACM Trans Database Syst. 1984;9(1): 38–71.CrossRefGoogle Scholar
  11. 11.
    Roussopoulos N, Leifker D. Direct spatial search on pictorial databases using packed R-trees. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1985. p. 17–31.CrossRefGoogle Scholar
  12. 12.
    Roussopoulos N, Kelley S, Vincent F. Nearest neighbor queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1995. p. 71–9.Google Scholar
  13. 13.
    Samet H. The design and analysis of spatial data structures. Addison-Wesley; Reading, MA, 1990.Google Scholar
  14. 14.
    Seeger B, Kriegel H-P. The Buddy-tree: an efficient and robust access method for spatial database systems. In: Proceedings of the 16th International Conference on Very Large Data Bases; 1990. p. 590–601.Google Scholar
  15. 15.
    Sellis T, Roussopoulos N, Faloutsos C. The R+-tree: a dynamic index for multidimensional objects. In: Proceedings of the 13th International Conference on Very Large Data Bases; 1987. p. 507–18.Google Scholar

Copyright information

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

Authors and Affiliations

  • Yannis Manolopoulos
    • 1
  • Yannis Theodoridis
    • 2
  • Vassilis J. Tsotras
    • 3
  1. 1.Aristotle University of ThessalonikiThessalonikiGreece
  2. 2.University of PiraeusPiraeusGreece
  3. 3.University of California-RiversideRiversideUSA

Section editors and affiliations

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