Skip to main content
Springer Nature Link
Log in

Optimizing Query Performance with Inverted Cache in Metric Spaces

  • Conference paper
  • First Online:
Advances in Databases and Information Systems (ADBIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9809))

Abstract

Similarity searching has become widely available in many on-line archives of multimedia content. Querying such systems starts with either a query object provided by user or a random object provided by the system, and proceeds in more iterations to improve user’s satisfaction with query results. This leads to processing many very similar queries by the system. In this paper, we analyze performance of two representatives of metric indexing structures and propose a novel concept of reordering search queue that optimizes access to data partitions for repetitive queries. This concept is verified in numerous experiments on real-life image dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://mufin.fi.muni.cz/imgsearch/similar.

References

  1. Amato, G., Rabitti, F., Savino, P., Zezula, P.: Region proximity in metric spaces and its use for approximate similarity search. ACM Trans. Inf. Syst. (TOIS) 21(2), 192–227 (2003)

    Article  Google Scholar 

  2. Barrios, J.M., Bustos, B., Skopal, T.: Analyzing and dynamically indexing the query set. Inf. Syst. 45, 37–47 (2014)

    Article  Google Scholar 

  3. Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., Zezula, P.: Building a web-scale image similarity search system. Multimedia Tools Appl. 47(3), 599–629 (2009)

    Article  Google Scholar 

  4. Beecks, C., Uysal, M.S., Driessen, P., Seidl, T.: Content-based exploration of multimedia databases. In: Proceedings of the 11th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 59–64. IEEE, June 2013

    Google Scholar 

  5. Beecks, C., Skopal, T., Schöffmann, K., Seidl, T.: Towards large-scale multimedia exploration. In: Proceedings of the 5th International Workshop on Ranking in Databases (DBRank), Seattle, WA, USA, pp. 31–33. VLDB Endowment (2011)

    Google Scholar 

  6. Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33(3), 322–373 (2001)

    Article  Google Scholar 

  7. Chávez, E., Marroquín, J.L., Navarro, G.: Overcoming the curse of dimensionality. In: Proceedings of the European Workshop on Content-Based Multimedia Indexing (CBMI), Toulouse, France, 25–27 October 1999, pp. 57–64 (1999)

    Google Scholar 

  8. Chávez, E., Navarro, G., Baeza-Yates, R.A., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. (CSUR) 33(3), 273–321 (2001)

    Article  Google Scholar 

  9. Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: Jarke, M., Carey, M.J., Dittrich, K.R., Lochovsky, F.H., Loucopoulos, P., Jeusfeld, M.A. (eds.) Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB), Athens, Greece, 25–29 August 1997, pp. 426–435. Morgan Kaufmann (1997)

    Google Scholar 

  10. Deepak, P., Prasad, M.D.: Operators for Similarity Search: Semantics, Techniques and Usage Scenarios. Springer, Heidelberg (2015)

    Google Scholar 

  11. Esuli, A.: Use of permutation prefixes for efficient and scalable approximate similarity search. Inf. Process. Manage. 48(5), 889–902 (2012)

    Article  Google Scholar 

  12. Houle, M.E., Nett, M.: Rank-based similarity search: reducing the dimensional dependence. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 136–150 (2015)

    Article  Google Scholar 

  13. Houle, M.E., Sakuma, J.: Fast approximate similarity search in extremely high-dimensional data sets. In: Proceedings of the 21st International Conference on Data Engineering (ICDE), pp. 619–630, April 2005

    Google Scholar 

  14. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2(1), 1–19 (2006)

    Article  Google Scholar 

  15. Moško, J., Lokoč, J., Grošup, T., Čech, P., Skopal, T., Lánský, J.: MLES: multilayer exploration structure for multimedia exploration. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds.) New Trends in Databases and Information Systems. Communications in Computer and Information Science, vol. 539, pp. 135–144. Springer, Switzerland (2015)

    Chapter  Google Scholar 

  16. Novak, D., Batko, M., Zezula, P.: Generic similarity search engine demonstrated by an image retrieval application. In: Proceedings of the 32nd International ACM Conference on Research and Development in Information Retrieval (SIGIR), Boston, MA, USA, p. 840. ACM (2009)

    Google Scholar 

  17. Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36, 721–733 (2011)

    Article  Google Scholar 

  18. Oliveira, P.H., Traina Jr., C., Kaster, D.S.: Improving the pruning ability of dynamic metric access methods with local additional pivots and anticipation of information. In: Morzy, T., Valduriez, P., Ladjel, B. (eds.) ADBIS 2015. LNCS, vol. 9282, pp. 18–31. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  19. Samet, H.: Foundations of Multidimensional And Metric Data Structures. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  20. Skopal, T., Lokoc, J., Bustos, B.: D-cache: universal distance cache for metric access methods. IEEE Trans. Knowl. Data Eng. 24(5), 868–881 (2012)

    Article  Google Scholar 

  21. 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)

    Chapter  Google Scholar 

  22. 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)

    Chapter  Google Scholar 

  23. Vilar, J.M.: Reducing the overhead of the AESA metric-space nearest neighbour searching algorithm. Inf. Process. Lett. 56(5), 265–271 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  24. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, New York (2005)

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by Czech Science Foundation project GA16-18889S.

Author information

Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Botanicka 68a, Brno, Czech Republic

    Matej Antol & Vlastislav Dohnal

Authors
  1. Matej Antol
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vlastislav Dohnal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matej Antol .

Editor information

Editors and Affiliations

  1. MFF, Charles University MFF, Prague, Czech Republic

    Jaroslav Pokorný

  2. Faculty of Sciences, University of Novi Sad Faculty of Sciences, Novi Sad, Serbia

    Mirjana Ivanović

  3. Christian-Albrechts-Universität Kiel , Kiel, Germany

    Bernhard Thalheim

  4. VSB-Technical University Ostrava , Ostrava, Czech Republic

    Petr Šaloun

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Antol, M., Dohnal, V. (2016). Optimizing Query Performance with Inverted Cache in Metric Spaces. In: Pokorný, J., Ivanović, M., Thalheim, B., Šaloun, P. (eds) Advances in Databases and Information Systems. ADBIS 2016. Lecture Notes in Computer Science(), vol 9809. Springer, Cham. https://doi.org/10.1007/978-3-319-44039-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44039-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44038-5

  • Online ISBN: 978-3-319-44039-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics