Horizontal Partitioning of Multimedia Databases Using Hierarchical Agglomerative Clustering

  • Lisbeth Rodríguez-Mazahua
  • Giner Alor-Hernández
  • Ma. Antonieta Abud-Figueroa
  • S. Gustavo Peláez-Camarena
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8857)


Horizontal partitioning is a database design technique widely used in relational databases in order to achieve query optimization. Recently, this technique has been applied in multimedia databases to improve query execution cost in these databases. Nevertheless, current algorithms are based on affinity between predicates to obtain an horizontal partitioning scheme (HPS). Affinity measures how a pair of predicates is accessed by the queries (“togetherness”). The main disadvantage of this measure is that it only involves two predicates, and hence does not show the “togetherness” of more than two predicates. In this paper we propose an horizontal partitioning method for multimedia databases which is based on a hierarchical agglomerative clustering algorithm. The main advantage of our method is that it does not use affinity to create the HPS. We present experimental results to clarify the soundness of the proposed method.


Horizontal partitioning Multimedia databases Hierarchical clustering 


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  1. 1.
    Bellatreche, L., Karlapalem, K., Simonet, A.: Algorithms and support for horizontal class partitioning in object-oriented databases. Distrib. Parallel Databases 8(2), 155–179 (2000)CrossRefGoogle Scholar
  2. 2.
    Ceri, S., Negri, M., Pelagatti, G.: Horizontal data partitioning in database design. In: Proceedings of the 1982 ACM SIGMOD International Conference on Management of Data, SIGMOD 1982, pp. 128–136. ACM, New York (1982)CrossRefGoogle Scholar
  3. 3.
    Chakravarthy, S., Muthuraj, J., Varadarajan, R., Navathe, S.B.: An objective function for vertically partitioning relations in distributed databases and its analysis. Distrib. Parallel Databases 2(2), 183–207 (1994)CrossRefGoogle Scholar
  4. 4.
    Chbeir, R., Laurent, D.: Towards a novel approach to multimedia data mixed fragmentation. In: Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES 2009, pp. 30:200–30:204. ACM, New York (2009)Google Scholar
  5. 5.
    Chbeir, R., Laurent, D.: Enhancing multimedia data fragmentation. Journal of Multimedia Processing and Technologies 1(2), 112–131 (2010)Google Scholar
  6. 6.
    Cheng, C.H., Lee, W.K., Wong, K.F.: A genetic algorithm-based clustering approach for database partitioning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32(3), 215–230 (2002)CrossRefGoogle Scholar
  7. 7.
    Chu, W.W., Ieong, I.T.: A transaction-based approach to vertical partitioning for relational database systems. IEEE Trans. Softw. Eng. 19(8), 804–812 (1993)CrossRefGoogle Scholar
  8. 8.
    Ezeife, C.I., Barker, K.: A comprehensive approach to horizontal class fragmentation in a distributed object based system. Distrib. Parallel Databases 3(3), 247–272 (1995)CrossRefGoogle Scholar
  9. 9.
    Getahun, F., Tekli, J., Atnafu, S., Chbeir, R.: The use of semantic-based predicates implication to improve horizontal multimedia database fragmentation. In: Workshop on Multimedia Information Retrieval on The Many Faces of Multimedia Semantics, MS 2007, pp. 29–38. ACM, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)Google Scholar
  11. 11.
    Khalil, N., Eid, D., Khair, M.: Availability and reliability issues in distributed databases using optimal horizontal fragmentation. In: Bench-Capon, T.J.M., Soda, G., Tjoa, A.M. (eds.) DEXA 1999. LNCS, vol. 1677, pp. 771–780. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Khan, S.I., Hoque, D.A.S.M.L.: A new technique for database fragmentation in distributed systems. International Journal of Computer Applications 5(9), 20–24 (2010)CrossRefGoogle Scholar
  13. 13.
    Ma, H.: Distribution Design for Complex Value Databases. Ph.D. thesis, Massey University (2007)Google Scholar
  14. 14.
    Murty, M., Rashmin, B., Bhattacharyya, C.: Clustering based on genetic algorithms. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds.) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. SCI, vol. 98, pp. 137–159. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Navathe, S., Karlapalem, K., Ra, M.: A mixed fragmentation methodology for initial distributed database design. Journal of Computer and Software Engineering 3(4), 395–426 (1995)Google Scholar
  16. 16.
    Navathe, S.B., Ra, M.: Vertical partitioning for database design: A graphical algorithm. In: Proceedings of the 1989 ACM SIGMOD International Conference on Management of Data, SIGMOD 1989, pp. 440–450. ACM, New York (1989)CrossRefGoogle Scholar
  17. 17.
    Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 3rd edn. Springer (2011)Google Scholar
  18. 18.
    Rodriguez, L., Li, X.: A vertical partitioning algorithm for distributed multimedia databases. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 544–558. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Rodríguez, L., Li, X., Cervantes, J., García-Lamont, F.: Dymond: An active system for dynamic vertical partitioning of multimedia databases. In: Proceedings of the 16th International Database Engineering & Applications Sysmposium, IDEAS 2012, pp. 71–80. ACM, New York (2012)Google Scholar
  20. 20.
    Saad, S., Tekli, J., Chbeir, R., Yétongnon, K.: Towards multimedia fragmentation. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds.) ADBIS 2006. LNCS, vol. 4152, pp. 415–429. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Shin, D.G., Irani, K.B.: Fragmenting relations horizontally using a knowledge-based approach. IEEE Trans. Softw. Eng. 17(9), 872–883 (1991)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Son, J.H., Kim, M.H.: An adaptable vertical partitioning method in distributed systems. Journal of Systems and Software 73(3), 551–561 (2004)CrossRefGoogle Scholar
  23. 23.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)Google Scholar
  24. 24.
    Zhang, Y., Orlowska, M.E.: On fragmentation approaches for distributed database design. Information Sciences - Applications 1(3), 117–132 (1994)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lisbeth Rodríguez-Mazahua
    • 1
  • Giner Alor-Hernández
    • 1
  • Ma. Antonieta Abud-Figueroa
    • 1
  • S. Gustavo Peláez-Camarena
    • 1
  1. 1.Division of Research and Postgraduate Studies, Instituto Tecnológico de OrizabaVeracruzMéxico

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