Evaluation of Static/Dynamic Cache for Similarity Search Engines

  • R. Solar
  • V. Gil-Costa
  • M. Marín
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9587)


In large scale search systems, where it is important to achieve a high query throughput, cache strategies are a feasible tool to achieve this goal. A number of efficient cache strategies devised for exact query search in different application domains have been proposed so far. In similarity query search on metric spaces it is necessary to consider additional design requirements devised to produce good quality approximate results from the cache content. In this paper, we propose a Static/Dynamic cache strategy for metric spaces which takes advantage of results of static cache miss operations and their associated distance evaluations for increasing the overall performance of the cache. We present an experimental evaluation of the performance obtained with our strategy for different query selection/replacement strategies.


Approximate similarity search Metric cache 



Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC (ECM-02). The authors would also like to thank to Basal funds FB0001, Conicyt, Chile; and Veronica Gil-Costa also thanks to PICT-2014-1146.


  1. 1.
    Amato, G., Gennaro, C., Savino, P.: MI-File: using inverted files for scalable approximate similarity search. Multimedia Tools Appl. 71(3), 1333–1362 (2014)CrossRefGoogle 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. CoRR, abs/0905.4627v2 (2009).
  3. 3.
    Brisaboa, N.R., Cerdeira-Pena, A., Gil-Costa, V., Marin, M., Pedreira, O.: Efficient similarity search by combining indexing and caching strategies. In: Italiano, G.F., Margaria-Steffen, T., Pokorný, J., Quisquater, J.-J., Wattenhofer, R. (eds.) SOFSEM 2015. LNCS, vol. 8939, pp. 486–497. Springer, Heidelberg (2015)Google Scholar
  4. 4.
    Chavez, E., Navarro, G.: An effective clustering algorithm to index high dimensional metric spaces. In: SPIRE, p. 75 (2000)Google Scholar
  5. 5.
    Chierichetti, F., Kumar, R., Vassilvitskii, S.: similarity caching. In: PODS, pp. 127–136 (2009)Google Scholar
  6. 6.
    Esuli, A.: Use of permutation prefixes for efficient and scalable approximate similarity search. IPM J. 48, 889–902 (2012)Google Scholar
  7. 7.
    Falchi, F., Lucchese, C., Orlando, S., Perego, R., Rabitti, F.: A metric cache for similarity search. In: LSDS-IR, pp. 43–50 (2008)Google Scholar
  8. 8.
    Falchi, F., Lucchese, C., Orlando, S., Perego, R., Rabitti, F.: Similarity caching in large-scale image retrieval. IPM J. 48, 803–818 (2012)Google Scholar
  9. 9.
    Gil-Costa, V., Marin, M.: Approximate distributed metric-space search. In: LSDS-IR, pp. 15–20 (2011)Google Scholar
  10. 10.
    Mohamed, H., Marchand-Maillet, S.: Permutation-based pruning for approximate K-NN search. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part I. LNCS, vol. 8055, pp. 40–47. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Pandey, S., Broder, A., Chierichetti, F., Josifovski, V., Kumar, R., Vassilvitskii, S.: Nearest-neighbor caching for content-match applications. In: WWW, pp. 441–450 (2009)Google Scholar
  12. 12.
    Silva, E., Teixeira, T., Teodoro, G., Valle, E.: Large-scale distributed locality-sensitive hashing for general metric data. In: Traina, A.J.M., Traina, Jr., C., Cordeiro, R.L.F. (eds.) SISAP 2014. LNCS, vol. 8821, pp. 82–93. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Skopal, T., Lokoc, J., Bustos, B.: D-Cache: universal distance cache for metric access methods. TKDE 24, 868–881 (2012)Google Scholar
  14. 14.
    Walters-Williams, J., Li, Y.: Comparative study of distance functions for nearest neighbors. In: Advanced Techniques in Computing Sciences and Software Engineering, pp. 79–84 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.CITIAPSUniversidad de Santiago de ChileSantiagoChile
  2. 2.Yahoo! Research Latin AmericaUNSL-CONICETSan LuisArgentina
  3. 3.DIINFUniversity of SantiagoSantiagoChile
  4. 4.Center for Biotechnology and BioengineeringUniversity of ChileSantiagoChile

Personalised recommendations