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Memory-Adaptative Dynamic Spatial Approximation Trees

  • Diego Arroyuelo
  • Francisca Muñoz
  • Gonzalo Navarro
  • Nora Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2857)

Abstract

Dynamic spatial approximation trees (dsa–trees) are efficient data structures for searching metric spaces. However, using enough storage, pivoting schemes beat dsa–trees in any metric space. In this paper we combine both concepts in a data structure that enjoys the features of dsa–trees and that improves query time by making the best use of the available memory. We show experimentally that our data structure is competitive for searching metric spaces.

Keywords

Search Time Query Time Hybrid Index Distance Evaluation Insertion Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Proximity searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  2. 2.
    Navarro, G., Reyes, N.: Fully dynamic spatial approximation trees. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 254–270. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Traina, C., Traina, A., Santos Filho, R., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: Proc. CIKM 2002, pp. 219–226 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Diego Arroyuelo
    • 1
  • Francisca Muñoz
    • 2
  • Gonzalo Navarro
    • 2
  • Nora Reyes
    • 1
  1. 1.Depto. de InformáticaUniv. Nac. de San LuisArgentina
  2. 2.Center for Web Research, Dept. of Computer ScienceUniv. of Chile 

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