Encyclopedia of Database Systems

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

Indexing Metric Spaces

  • Pavel ZezulaEmail author
  • Michal Batko
  • Vlastislav Dohnal
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_199


Distance indexing


Metric space indexing is closely related to the recent digitization revolution where almost everything that one can see, hear, read, write or measure is available in digital form. Unlike traditional attribute-like data types such as numbers and strings of sortable domains, instances of these new data types are complex, and the only measure of comparison to apply is a sort of similarity. Such a situation implies an application of the query- by- example search paradigm where the database is searched for objects that are near the example object, also called the query object. A useful abstraction of this similarity is to see it as mathematical metric space [ 7]. The problem of organizing and searching large datasets of complex objects can then be considered from the perspective of generic or arbitrary metric spaces, sometimes labeled distance spaces. In general, the search problem can be described as follows:

Let D be a domain, d a distance measure on D, and...

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Recommended Reading

  1. 1.
    Batko M, Novak D, Falchi F, Zezula P. On scalability of the similarity search in the world of peers. In: Proceedings of the 1st International Conference Scalable Information Systems; 2006. p. 1–12.Google Scholar
  2. 2.
    Chávez E, Navarro G, Baeza-Yates R, Marroquín JL. Searching in metric spaces. ACM Comput Surv. 2001;33(3):273–321.CrossRefGoogle Scholar
  3. 3.
    Ciaccia P, Patella M, Zezula P. M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23th International Conference on Very Large Data Bases; 1997. p. 426–35.Google Scholar
  4. 4.
    Dohnal V, Gennaro C, Savino P, Zezula P. D-Index: distance searching index for metric data sets. Multimedia Tools Appl. 2003;21(1):9–33.CrossRefGoogle Scholar
  5. 5.
    Hjaltason GR, Samet H. Incremental similarity search in multimedia databases. Technical Report CS-TR-4199, Computer Science Department, University of Maryland, College Park; Nov 2000.Google Scholar
  6. 6.
    Hjaltason GR, Samet H. Index-driven similarity search in metric spaces. ACM Trans Database Syst. 2003;28(4):517–80.CrossRefGoogle Scholar
  7. 7.
    Kelly JL. General topology. New York: D. Van Nostrand; 1955.Google Scholar
  8. 8.
    Samet H. Foundations of multidimensional and metric data structures. San Francisco: Morgan Kaufmann; 2005.zbMATHGoogle Scholar
  9. 9.
    Traina Jr C, Traina AJM, Seeger B, Faloutsos C. Slim-trees: high performance metric trees minimizing overlap between nodes. In: Advances in Database Technology, Proceedings of the 7th International Conference on Extending Database Technology; 2000. p. 51–65.CrossRefGoogle Scholar
  10. 10.
    Zezula P, Amato G, Dohnal V, Batko M. Similarity search: the metric space approach. Berlin: Springer; 2006.zbMATHGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Masaryk UniversityBrnoCzech Republic

Section editors and affiliations

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