Advertisement

The MM-Tree: A Memory-Based Metric Tree Without Overlap Between Nodes

  • Ives Rene Venturini Pola
  • Caetano TrainaJr.
  • Agma Juci Machado Traina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4690)

Abstract

Advanced database systems offer similarity queries on complex data. Searching by similarity on complex data is accelerated through the use of metric access methods (MAM). These access methods organize data in order to reduce the number of comparison between elements when answering queries. MAM can be categorized in two types: disk-based and memory-based. The disk-based structures limit the partitioning of space forcing nodes to have multiple elements according to disk page sizes. However, memory-based trees allows more flexibility, producing trees faster to build and to perform queries. Although recent developments target disk-based methods on tree structures, several applications benefits from a faster way to build indexes on main memory. This paper presents a memory-based metric tree, the MM-tree, which successively partitions the space into non-overlapping regions. We present experiments comparing MM-tree with existing high performance MAM, including the disk-based Slim-tree. The experiments reveal that MM-tree requires up to one fifth of the number of distance calculations to be constructed when compared with Slim-tree, performs range queries requiring 64% less distance calculations and KNN queries requiring 74% less distance calculations.

Keywords

Main Memory Distance Calculation Range Query Point 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berchtold, S., Keim, D.A., Kriegel, H.P.: The x-tree: An index structure for high-dimensional data. In: VLDB, Bombay, India, pp. 28–39. Morgan Kaufmann, San Francisco (1996)Google Scholar
  2. 2.
    Chakrabarti, K., Mehrotra, S.: The hybrid tree: An index structure for high dimensional feature spaces. In: IEEE (ICDE), Sydney, Australia, pp. 440–447. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  3. 3.
    Katayama, N., Satoh, S.: The sr-tree: An index structure for high-dimensional nearest neighbor queries. In: Peckham, J. (ed.) ACM SIGMOD, Tucson, Arizona, USA, pp. 369–380. ACM Press, New York (1997)CrossRefGoogle Scholar
  4. 4.
    Lin, K.I.D., Jagadish, H.V., Christos, F.: The tv-tree: An index structure for high-dimensional data. VLDB Journal 3(4), 517–542 (1994)CrossRefGoogle Scholar
  5. 5.
    Gaede, V., Gunther, O.: Multidimensional access methods. ACM Computing Surveys 30(2), 170–231 (1998)CrossRefGoogle Scholar
  6. 6.
    Burkhard, W.A., Keller, R.M.: Some approaches to best-match file searching. Communications of the ACM (CACM) 16(4), 230–236 (1973)zbMATHCrossRefGoogle Scholar
  7. 7.
    Chávez, E., Navarro, G., Baeza-Yates, R.A., Marroquín, J.L.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)CrossRefGoogle Scholar
  8. 8.
    Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Information Processing Letters 40(4), 175–179 (1991)zbMATHCrossRefGoogle Scholar
  9. 9.
    Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: ACM/SIGACT-SIAM (SODA), Austin, TX, pp. 311–321 (1993)Google Scholar
  10. 10.
    Bozkaya, T., Özsoyoglu, Z.M.: Distance-based indexing for high-dimensional metric spaces. In: ACM SIGMOD, Tucson, AZ, pp. 357–368. ACM Press, New York (1997)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Traina Jr., 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
  13. 13.
    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
  14. 14.
    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 (ICDE), Heidelberg, Germany, pp. 623–630. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  15. 15.
    Traina Jr., C., Traina, A.J.M., Santos Filho, R.F., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: CIKM, McLean, VA, USA, pp. 219–226. ACM Press, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ives Rene Venturini Pola
    • 1
  • Caetano TrainaJr.
    • 1
  • Agma Juci Machado Traina
    • 1
  1. 1.Computer Science Department - ICMC, University of Sao Paulo at Sao CarlosBrazil

Personalised recommendations