Analysing Metric Data Structures Thinking of an Efficient GPU Implementation

  • Roberto Uribe-Paredes
  • Enrique Arias
  • Diego Cazorla
  • José Luis Sánchez
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)


Similarity search is becoming a field of interest because it can be applied to different areas in science and engineering. In real applications, when large volumes of data are processing, query response time can be quite high. In this case, it is necessary to apply mechanisms to significantly reduce the average query response time. For that purpose, modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, the authors make a comparative study of the most popular pivot selection methods in order to stablish a set of attractive features from the point of view of future GPU implementations.


Clustering-based methods Comparative study Data structures Metric spaces Pivot-based methods Range queries Similarity search. 



This work has been supported by the Ministerio de Ciencia e Innovación, project SATSIM (Ref: CGL2010-20787-C02-02), Spain and Research Center, University of Magallanes, Chile. Also, this work has been partially supported by CAPAP-H3 Network (TIN2010-12011-E).


  1. 1.
    Chávez E, Navarro G, Baeza-Yates R, Marroquín JL (2001) Searching in metric spaces. ACM Comput Surv 33(3):273–321Google Scholar
  2. 2.
    Kalantari I, McDonald G (1983) A data structure and an algorithm for the nearest point problem. IEEE Trans Software Eng 9(5):631–634MATHCrossRefGoogle Scholar
  3. 3.
    Uhlmann J (1991) Satisfying general proximity/similarity queries with metric trees. Inf Process Lett 40:175–179MATHCrossRefGoogle Scholar
  4. 4.
    Ciaccia P, Patella M, Zezula P (1997) M-tree : an efficient access method for similarity search in metric spaces. In: Proceedings of 23rd international conference on VLDB, 426–435Google Scholar
  5. 5.
    Brin S (1995) Near neighbor search in large metric spaces. In: Proceedings of the 21st VLDB conference, Morgan Kaufmann Publishers, 574–584, 1995Google Scholar
  6. 6.
    Navarro G, Uribe-Paredes R (2011) Fully dynamic metric access methods based on hyperplane partitioning. Inf Syst 36(4):734–747CrossRefGoogle Scholar
  7. 7.
    Micó L, Oncina J, Vidal E (1994) A new version of the nearest-neighbor approximating and eliminating search (aesa) with linear preprocessing-time and memory requirements. Pattern Recogn Lett 15:9–17CrossRefGoogle Scholar
  8. 8.
    Baeza-Yates R, Cunto W, Manber U, Wu S (1994) Proximity matching using fixed queries trees.In 5th Combinatorial Pattern Matching (CPM’94). LNCS 807:198–212MathSciNetGoogle Scholar
  9. 9.
    Chávez E, Marroquín J, Navarro G (2001a) Fixed queries array: a fast and economical data structure for proximity searching. Multimedia Tools Appl 14(2):113–135MATHCrossRefGoogle Scholar
  10. 10.
    Pedreira O, Brisaboa NR (2007) Spatial selection of sparse pivots for similarity search in metric spaces. In: Proceedings of the 33rd conference on current trends in theory and practice of computer science (SOFSEM (2007) LNCS, vol 4362. Czech Republic, Springer, Harrachov, pp 434–445Google Scholar
  11. 11.
    Uribe-Paredes R, Cazorla D, Sánchez JL, Arias E (2012) A comparative study of different metric structures: thinking on gpu implementations. In: Proceedings of the world congress on engineering (2012) WCE 2012. Lecture notes in engineering and computer science, England, London, pp 312–317Google Scholar
  12. 12.
    Chávez E, Marroquín J, Baeza-Yates R (1999) Spaghettis: an array based algorithm for similarity queries in metri spaces. In: Proceedings of 6th international symposium on String Processing and Information Retrieval (SPIRE’99), IEEE CS Press, pp 38–46Google Scholar
  13. 13.
    Micó L, Oncina J, Carrasco R (1996) A fast branch and bound nearest neighbor classiffier in metric spaces. Pattern Recogn Lett 17:731–739CrossRefGoogle Scholar
  14. 14.
    Hetland M (2009) The basic principles of metric indexing. In: Coello CA, Dehuri S, Ghosh, S (eds) Swarm intelligence for multi-objective problems in data mining, Studies in computational intelligence, vol 242. Springer Berlin, pp 199–232Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Roberto Uribe-Paredes
    • 1
  • Enrique Arias
    • 2
  • Diego Cazorla
    • 2
  • José Luis Sánchez
    • 2
  1. 1.Computer Engineering DepartmentUniversity of MagallanesPunta ArenasChile
  2. 2.Computing Systems DepartmentUniversity of Castilla-La ManchaAlbaceteSpain

Personalised recommendations