Improving the Pruning Ability of Dynamic Metric Access Methods with Local Additional Pivots and Anticipation of Information

  • Paulo H. Oliveira
  • Caetano TrainaJr.
  • Daniel S. Kaster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9282)


Metric Access Methods (MAMs) have been proved to allow performing similarity queries over complex data more efficiently than other access methods. They can be considered dynamic or static depending on the pivot type used in their construction. Global pivots tend to compromise the dynamicity of MAMs, as eventual pivot-related updates must be propagated through the entire structure, while local pivots allow this maintenance to occur locally. Several applications handle online complex data and, consequently, demand efficient dynamic indexes to be successful. In this context, this work presents two techniques for improving the pruning ability of dynamic MAMs: (i) using cutting local additional pivots to reduce distance calculations and (ii) anticipating information from child nodes to reduce unnecessary disk accesses. The experiments reveal significant improvements in a dynamic MAM, reducing execution time in more than 50 % for similarity queries posed on datasets ranging from moderate to high dimensionality and cardinality.


Similarity queries Metric access methods Cutting local additional pivots Anticipation of child information 


  1. 1.
    Batko, M., Kohoutkova, P., Novak, D.: CoPhIR image collection under the microscope. In: 2nd International Workshop on Similarity Search and Applications, pp. 47–54. IEEE Computer Society, Washington, DC (2009)Google Scholar
  2. 2.
    Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., Rabitti, F.: CoPhIR: A Test Collection for Content-Based Image Retrieval. Computing Research Repository abs/0905.4627v2 (2009)Google Scholar
  3. 3.
    Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. Commun. ACM 16(4), 230–236 (1973)CrossRefzbMATHGoogle Scholar
  4. 4.
    Carélo, C.C.M., Pola, I.R.V., Ciferri, R.R., Traina, A.J.M., Traina Jr., C., Ciferri, C.D.A.: Slicing the metric space to provide quick indexing of complex data in the main memory. Inf. Syst. 36(1), 79–98 (2011)CrossRefGoogle Scholar
  5. 5.
    Ciaccia, P., Patella, M., Zezula, P.: M-Tree: An efficient access method for similarity search in metric spaces. In: 23rd International Conference on Very Large Data Bases, pp. 426–435. Morgan Kaufmann, San Francisco (1997)Google Scholar
  6. 6.
    Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Esuli, A.: Use of permutation prefixes for efficient and scalable approximate similarity search. Inf. Process. Manage. 48(5), 889–902 (2012)CrossRefGoogle Scholar
  8. 8.
    Faloutsos, C.: Searching Multimedia Databases by Content. Advances in Database Systems, vol. 3. Springer, New York (1996)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gaede, V., Gunther, O.: Multidimensional access methods. ACM Comput. Surv. 30(2), 170–231 (1998)CrossRefGoogle Scholar
  10. 10.
    Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The amsterdam library of object images. Int. J. Comput. Vis. 61(1), 103–112 (2005)CrossRefGoogle Scholar
  11. 11.
    Pola, I.R.V., Traina Jr., C., Traina, A.J.M.: The MM-tree: a memory-based metric tree without overlap between nodes. In: Ioannidis, Y., Novikov, B., Rachev, B. (eds.) ADBIS 2007. LNCS, vol. 4690, pp. 157–171. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  12. 12.
    Skopal, T., Hoksza, D.: Improving the performance of M-Tree family by nearest-neighbor graphs. In: Ioannidis, Y., Novikov, B., Rachev, B. (eds.) ADBIS 2007. LNCS, vol. 4690, pp. 172–188. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  13. 13.
    Skopal, T., Pokorný, J., Snášel, V.: Nearest neighbours search using the PM-tree. In: Zhou, L., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 803–815. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  14. 14.
    Traina Jr., C., Filho, R.F.S., Traina, A.J.M., Vieira, M.R., Faloutsos, C.: The omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient. VLDB J. 16(4), 483–505 (2007)CrossRefGoogle Scholar
  15. 15.
    Traina Jr., C., Traina, A.J.M., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric data sets using slim-trees. IEEE Trans. Knowl. Data Eng. 14(2), 244–260 (2002)CrossRefGoogle Scholar
  16. 16.
    Traina Jr., C., Traina, A.J.M., Filho, R.F.S., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: 11th International Conference on Information and Knowledge Management, pp. 219–226. ACM, New York (2002)Google Scholar
  17. 17.
    Vieira, M.R., Traina Jr., C., Chino, F.J.T., Traina, A.J.M.: DBM-Tree: trading height-balancing for performance in metric access methods. J. Braz. Comput. Soc. 11(3), 37–51 (2005)CrossRefGoogle Scholar
  18. 18.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, New York (2006) zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paulo H. Oliveira
    • 1
  • Caetano TrainaJr.
    • 2
  • Daniel S. Kaster
    • 1
  1. 1.Department of Computer ScienceUniversity of Londrina (UEL)LondrinaBrazil
  2. 2.Institute of Mathematics and Computer ScienceUniversity of São Paulo (USP)São PauloBrazil

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