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International Symposium on Parallel and Distributed Processing and Applications

ISPA 2007: Frontiers of High Performance Computing and Networking ISPA 2007 Workshops pp 285–294Cite as

A Data Allocation Method for Efficient Content-Based Retrieval in Parallel Multimedia Databases

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4743))

Abstract

Scaling up to large multimedia databases with high dimensional metadata descriptions while providing fast content-based retrieval (CBR) is getting increasingly important for many applications. To address this objective, we strive to exploit the popular parallel shared-nothing architecture. In this context, a major problem is data allocation on the different nodes in order to yield efficient parallel content-based retrieval. In this paper, assuming a clustering process and based on a complexity analysis of CBR, we propose a data allocation method with an optimal number of clusters and nodes. We validated our method through experiments with different high dimensional synthetic databases and implemented a query processing algorithm for full k nearest neighbors.

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Authors and Affiliations

  1. INRIA and LINA, Université de Nantes,  

    Jorge Manjarrez-Sanchez, José Martinez & Patrick Valduriez

Authors
  1. Jorge Manjarrez-Sanchez
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  2. José Martinez
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  3. Patrick Valduriez
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Editor information

Parimala Thulasiraman Xubin He Tony Li Xu Mieso K. Denko Ruppa K. Thulasiram Laurence T. Yang

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© 2007 Springer-Verlag Berlin Heidelberg

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Manjarrez-Sanchez, J., Martinez, J., Valduriez, P. (2007). A Data Allocation Method for Efficient Content-Based Retrieval in Parallel Multimedia Databases. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds) Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. ISPA 2007. Lecture Notes in Computer Science, vol 4743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74767-3_30

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  • DOI: https://doi.org/10.1007/978-3-540-74767-3_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74766-6

  • Online ISBN: 978-3-540-74767-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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