MESSIF: Metric Similarity Search Implementation Framework

  • Michal Batko
  • David Novak
  • Pavel Zezula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4877)


The similarity search has become a fundamental computational task in many applications. One of the mathematical models of the similarity – the metric space – has drawn attention of many researchers resulting in several sophisticated metric-indexing techniques. An important part of a research in this area is typically a prototype implementation and subsequent experimental evaluation of the proposed data structure. This paper describes an implementation framework called MESSIF that eases the task of building such prototypes. It provides a number of modules from basic storage management, over a wide support for distributed processing, to automatic collecting of performance statistics. Due to its open and modular design it is also easy to implement additional modules, if necessary. The MESSIF also offers several ready-to-use generic clients that allow to control and test the index structures.


Range Query Distribute Hash Table Node Overlay Operation Execution Query Operation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  2. 2.
    Dohnal, V.: Indexing Structures fro Searching in Metric Spaces. PhD thesis, Faculty of Informatics, Masaryk University in Brno, Czech Republic (May 2004)Google Scholar
  3. 3.
    Hjaltason, G.R., Samet, H.: Index-driven similarity search in metric spaces. In: TODS 2003. ACM Transactions on Database Systems, vol. 28(4), pp. 517–580 (2003)Google Scholar
  4. 4.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: Proceedings of VLDB 1997, August 25-29, 1997, pp. 426–435. Morgan Kaufmann, Athens, Greece (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 21(1), 9–33 (2003)CrossRefGoogle Scholar
  6. 6.
    Batko, M., Novak, D., Falchi, F., Zezula, P.: On scalability of the similarity search in the world of peers. In: Proceedings of INFOSCALE 2006, May 30–June 1, 2006, pp. 1–12. ACM Press, New York (2006)Google Scholar
  7. 7.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of ACM SIGCOMM, pp. 149–160. ACM Press, San Diego, CA, USA (2001)Google Scholar
  8. 8.
    Aspnes, J., Shah, G.: Skip graphs. In: Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 384–393 (January 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michal Batko
    • 1
  • David Novak
    • 1
  • Pavel Zezula
    • 1
  1. 1.Masaryk University, BrnoCzech Republic

Personalised recommendations