Advertisement

A New Approach for Optimization of Dynamic Metric Access Methods Using an Algorithm of Effective Deletion

  • Renato Bueno
  • Daniel dos Santos Kaster
  • Agma Juci Machado Traina
  • Caetano TrainaJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5069)

Abstract

The existing Metric Access Methods (MAM) assume the data elements represent immutable objects. However, many applications must handle complex data evolving over time. Health care, weather monitoring, and other applications require removing or updating elements. Most of the MAM presented in the literature either do not have the deletion operation described, or it is performed just marking the element as deleted without effectively removing it from the structure. In this paper we describe an algorithm that effectively removes any element from a metric tree. While maintaining the height-balancing of the structure, the proposed deletion algorithm uses mechanisms to enforce a reduced number of pages in the tree, improving the query performance. Based on the deletion algorithm, we propose a new way to optimize a MAM, which we call the Push-pull technique. It reduces the node overlap performing the deletion and reinsertion of elements close to the border of each node covering region. We also developed the Smart Push-pull algorithm, which uses statistical data about subtrees’ overlapping to calculate how many elements should be removed from each node. The statistics are collected during the evaluation of the structure overlap, an operation employed to ascertain the need to trigger an optimization process. The experiments were run on the Slim-tree and showed a reduction of overlap and a query performance improvement over trees optimized by this technique as compared over trees optimized by the Slim-down method.

Keywords

Leaf Node Distance Calculation Point Query Access Method Covering Radius 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Faloutsos, C.: Indexing of multimedia data. In: Multimedia Databases in Perspective, pp. 219–245. Springer, Heidelberg (1997)Google Scholar
  2. 2.
    Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. CACM 16(4), 230–236 (1973)zbMATHGoogle Scholar
  3. 3.
    Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Information Processing Letters 40(4), 175–179 (1991)zbMATHCrossRefGoogle Scholar
  4. 4.
    Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: ACM/SIAM/SODA, Austin, TX, USA, pp. 311–321. ACM, New York (1993)Google Scholar
  5. 5.
    Bozkaya, T., Özsoyoglu, Z.M.: Indexing large metric spaces for similarity search queries. ACM TODS 24(3), 361–404 (1999)CrossRefGoogle Scholar
  6. 6.
    Brin, S.: Near neighbor search in large metric spaces. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) VLDB, Zurich, Switzerland, pp. 574–584. Morgan Kaufmann, San Francisco (1995)Google Scholar
  7. 7.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: Jarke, M. (ed.) VLDB, Athens, Greece, pp. 426–435. Morgan Kaufmann, San Francisco (1997)Google Scholar
  8. 8.
    Traina, J.C., Traina, A.J.M., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric datasets using Slim-trees. IEEE TKDE 14(2), 244–260 (2002)Google Scholar
  9. 9.
    Vieira, M.R., Traina Jr., C., Traina, A.J.M., Chino, F.J.T.: DBM-tree: A dynamic metric access method sensitive to local density data. In: Lifschitz, S. (ed.) SBBD, Brasília, DF, Brazil, pp. 33–47. SBC (2004)Google Scholar
  10. 10.
    Guttman, A.: R-tree: A dynamic index structure for spatial searching. In: ACM SIGMOD, Boston, MA, USA, pp. 47–57. ACM, New York (1984)Google Scholar
  11. 11.
    Skopal, T., Pokorný, J., Krátký, M., Snásel, V.: Revisiting M-tree building principles. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 148–162. Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: An efficient and robust access method for points and rectangles. In: Garcia-Molina, H., Jagadish, H.V. (eds.) ACM SIGMOD, Atlantic City, NJ, USA, pp. 322–331 (1990)Google Scholar
  13. 13.
    Ferreira, M.R.P., Bueno, R., Traina Jr., C.: DBGen - Gerador de dados sintéticos com distribuição fractal. In: Brayner, N., Dorneles, C.F. (eds.) Demo session - SBBD, Uberlândia, MG, Brazil, pp. 25–30 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Renato Bueno
    • 1
  • Daniel dos Santos Kaster
    • 2
  • Agma Juci Machado Traina
    • 1
  • Caetano TrainaJr.
    • 1
  1. 1.Department of Computer ScienceUniversity of São Paulo at São CarlosBrazil
  2. 2.Department of Computer ScienceUniversity of LondrinaLondrinaBrazil

Personalised recommendations