Horizontal Partitioning of Multimedia Databases Using Hierarchical Agglomerative Clustering View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Lisbeth Rodríguez-Mazahua , Giner Alor-Hernández , Ma. Antonieta Abud-Figueroa , S. Gustavo Peláez-Camarena

ABSTRACT

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. More... »

PAGES

296-309

References to SciGraph publications

  • 2002-06-18. Availability and Reliability Issues in Distributed Databases Using Optimal Horizontal Fragmentation in DATABASE AND EXPERT SYSTEMS APPLICATIONS
  • 1994-04. An objective function for vertically partitioning relations in distributed databases and its analysis in DISTRIBUTED AND PARALLEL DATABASES
  • 2006. Towards Multimedia Fragmentation in ADVANCES IN DATABASES AND INFORMATION SYSTEMS
  • 1995-07. A comprehensive approach to horizontal class fragmentation in a Distributed Object Based System in DISTRIBUTED AND PARALLEL DATABASES
  • 2011. A Vertical Partitioning Algorithm for Distributed Multimedia Databases in DATABASE AND EXPERT SYSTEMS APPLICATIONS
  • 2000-04. Algorithms and Support for Horizontal Class Partitioning in Object-Oriented Databases in DISTRIBUTED AND PARALLEL DATABASES
  • 2008. Clustering Based on Genetic Algorithms in MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR KNOWLEDGE DISCOVERY FROM DATABASES
  • Book

    TITLE

    Nature-Inspired Computation and Machine Learning

    ISBN

    978-3-319-13649-3
    978-3-319-13650-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-13650-9_27

    DOI

    http://dx.doi.org/10.1007/978-3-319-13650-9_27

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039801551


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