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Metric Indexing Assisted by Short-Term Memories

  • Humberto RazenteEmail author
  • Régis Michel Santos Sousa
  • Maria Camila Nardini Barioni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

Similarity queries are fundamental operations for applications that deal with complex data. This work proposes a new approach, called MIA (Metric Indexing Assisted by auxiliary memory with limited capacity), that can be employed to create dynamic metric access methods, such as M-trees and Slim-trees, through a short-term memory. We propose three strategies that were evaluated with various datasets and employing different node split policies. Experimental results show that metric access methods built with the MIA approach present better distribution of the elements in the index nodes when compared to the access methods built without it. Moreover, these results show the strategies decrease the overlap, the number of distance calculations, the number of disk accesses and the execution time to run k-nearest neighbor queries.

References

  1. 1.
    Arboretum library. https://bitbucket.org/gbdi/arboretum. Accessed July 2018
  2. 2.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: International Conference on Very Large Data Bases (VLDB), Greece, Athens, pp. 426–435 (1997)Google Scholar
  3. 3.
    Figueroa, K., Navarro, G., Chávez, E.: Metric spaces library (2007). http://www.sisap.org/Metric_Space_Library.html
  4. 4.
    Gama, J.: A survey on learning from data streams: current and future trends. Progr. Artif. Intell. 1(1), 45–55 (2012)CrossRefGoogle Scholar
  5. 5.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: International Confernence on Management of Data (SIGMOD), Boston, MA, pp. 47–57 (1984)Google Scholar
  6. 6.
    Lichman, M.: UCI Machine Learning Repository. School of Information and Computer Sciences, University of California, Irvine (2013). http://archive.ics.uci.edu/ml
  7. 7.
    Lokoc, J., Mosko, J., Cech, P., Skopal, T.: On indexing metric spaces using cut-regions. Inf. Syst. 43, 1–19 (2014)CrossRefGoogle Scholar
  8. 8.
    Lokoc, J., Skopal, T.: On reinsertions in m-tree. In: International Workshop on Similarity Search and Applications (SISAP), pp. 121–128. IEEE (2008)Google Scholar
  9. 9.
    Navarro, G., Reyes, N.: New dynamic metric indices for secondary memory. Inf. Syst. 59, 48–78 (2016)CrossRefGoogle Scholar
  10. 10.
    Ng, R.T., Han, J.: CLARANS: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002)CrossRefGoogle Scholar
  11. 11.
    Oliveira, P.H., Traina, C., Kaster, D.S.: Improving the pruning ability of dynamic metric access methods with local additional pivots and anticipation of information. In: Tadeusz, M., Valduriez, P., Bellatreche, L. (eds.) ADBIS 2015. LNCS, vol. 9282, pp. 18–31. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23135-8_2CrossRefGoogle Scholar
  12. 12.
    Razente, H.L., Lima, R.L.B., Barioni, M.C.N.: Similarity search through one-dimensional embeddings. In: ACM Symposium on Applied Computing (SAC), Marrakech, Morocco, pp. 874–879 (2017)Google Scholar
  13. 13.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: International Conference on Management of Data (SIGMOD), San Jose, pp. 71–79 (1995)Google Scholar
  14. 14.
    Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)zbMATHGoogle Scholar
  15. 15.
    Silva, Y.N., Aref, W.G., Larson, P.-A., Pearson, S., Ali, M.H.: Similarity queries: their conceptual evaluation, transformations, and processing. VLDB J. 22(3), 395–420 (2013)CrossRefGoogle Scholar
  16. 16.
    Skopal, T.: On fast non-metric similarity search by metric access methods. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 718–736. Springer, Heidelberg (2006).  https://doi.org/10.1007/11687238_43CrossRefGoogle Scholar
  17. 17.
    Souza, J., Razente, H., Barioni, M.C.: Optimizing metric access methods for querying and mining complex data types. J. Braz. Comput. Soc. (JBCS) 20(17), 14 (2014)Google Scholar
  18. 18.
    Traina, C., Traina, A., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric data sets using slim-trees. IEEE Trans. Knowl. Data Eng. (TKDE) 14(2), 244–260 (2002)CrossRefGoogle Scholar
  19. 19.
    Traina, C., Traina, A., Filho, R.S., Faloutsos, C.: How to improve the pruning ability of dynamic metric access methods. In: International Conference on Information and Knowledge Management (CIKM), McLean, pp. 219–226 (2002)Google Scholar
  20. 20.
    Traina, C., Traina, A., Seeger, B., Faloutsos, C.: Slim-trees: high performance metric trees minimizing overlap between nodes. In: International Conference on Extending Database Technology (EDBT), Konstanz, pp. 51–65 (2000)Google Scholar
  21. 21.
    Vespa, T., Traina, C., Traina, A.: Efficient bulk-loading on dynamic metric access methods. Inf. Syst. 35(5), 557–569 (2010)CrossRefGoogle Scholar
  22. 22.
    Vieira, M.R., Traina, C., Chino, F.J.T., Traina, A.: DBM-tree: a dynamic metric access method sensitive to local density data. J. Inf. Data Manag. (JIDM) 1(1), 111–127 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculdade de Computação (FACOM)Universidade Federal de Uberlândia (UFU)UberlândiaBrazil

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