Improving Metric Access Methods with Bucket Files

  • Ives R. V. Pola
  • Agma J. M. Traina
  • Caetano TrainaJr.
  • Daniel S. Kaster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9371)


Modern applications deal with complex data, where retrieval by similarity plays an important role in most of them. Complex data whose primary comparison mechanisms are similarity predicates are usually immersed in metric spaces. Metric Access Methods (MAMs) exploit the metric space properties to divide the metric space into regions and conquer efficiency on the processing of similarity queries, like range and k-nearest neighbor queries.

Existing MAM use homogeneous data structures to improve query execution, pursuing the same techniques employed by traditional methods developed to retrieve scalar and multidimensional data. In this paper, we combine hashing and hierarchical ball partitioning approaches to achieve a hybrid index that is tuned to improve similarity queries targeting complex data sets, with search algorithms that reduce total execution time by aggressively reducing the number of distance calculations. We applied our technique in the Slim-tree and performed experiments over real data sets showing that the proposed technique is able to reduce the execution time of both range and k-nearest queries to at least half of the Slim-tree. Moreover, this technique is general to be applied over many existing MAM.


Hash Function Leaf Node Distance Calculation Range Query Access Method 
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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  1. 1.
    Almeida, J., Torres, R.d.S., Leite, N.J.: Bp-tree: an efficient index for similarity search in high-dimensional metric spaces. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1365–1368. ACM, New York (2010)Google Scholar
  2. 2.
    Bozkaya, T., Özsoyoglu, Z.M.: Distance-based indexing for high-dimensional metric spaces. In: ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pp. 357–368. ACM Press (1997)Google Scholar
  3. 3.
    Brin, S.: Near neighbor search in large metric spaces. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) International Conference on Very Large Databases (VLDB), pp. 574–584. Morgan Kaufmann, Zurich (1995)Google Scholar
  4. 4.
    Ciaccia, P, Patella, M., Rabitti, F., Zezula, P.: Indexing metric spaces with m-tree. In: Atti del Quinto Convegno Nazionale SEBD, Verona, Italy, pp. 67–86 (1997)Google Scholar
  5. 5.
    Dohnal, V., Gennaro, C., Savino, P., Zezula, P.: D-index: Distance searching index for metric data sets. Multimedia Tools and Applications Journal (MTAJ) 21(1), 9–33 (2003)CrossRefGoogle Scholar
  6. 6.
    Faloutsos, C.: Indexing of multimedia data. In: Multimedia Databases in Perspective, pp. 219–245. Springer Verlag (1997)Google Scholar
  7. 7.
    Gennaro, C., Savino, P., Zezula, P.: Similarity search in metric databases through hashing. In: 3rd International Workshop on Multimedia Information Retrieval, Ottawa, Canada, pp. 1–5 (2001)Google Scholar
  8. 8.
    Kelley, J.L.: General Topology. Springer (1955)Google Scholar
  9. 9.
    Micó, L., Oncina, J., Vidal, E.: A new version of the nearest-neighbor approximating and eliminating search (aesa) with linear processing-time and memory requirements. Pattern Recognition Letters 15, 9–17 (1994)CrossRefGoogle Scholar
  10. 10.
    Navarro, G., Uribe-Paredes, R.: Fully dynamic metric access methods based on hyperplane partitioning. Inf. Syst. 36, 734–747 (2011)CrossRefGoogle Scholar
  11. 11.
    Santos Filho, R.F., Traina, A.J.M., Traina Jr., C., Faloutsos, C.: Similarity search without tears: the omni family of all-purpose access methods. In: IEEE International Conference on Data Engineering (ICDE), Heidelberg, Germany, pp. 623–630. IEEE Computer Society (2001)Google Scholar
  12. 12.
    Skopal, T.: Where are you heading, metric access methods?: a provocative survey. In: Proceedings of the Third International Conference on SImilarity Search and APplications, SISAP 2010, pp. 13–21. ACM, New York (2010)Google Scholar
  13. 13.
    Traina Jr, C., Traina, A.J.M., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric datasets using slim-trees. IEEE Transactions on Knowledge and Data Engineering (TKDE) 14(2), 244–260 (2002)CrossRefGoogle Scholar
  14. 14.
    Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Fourth Annual ACM/SIGACT-SIAM Symposium on Discrete Algorithms (SODA), Austin, TX, pp. 311–321 (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of São PauloSão CarlosBrazil
  2. 2.University of LondrinaLondrinaBrazil

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