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A Parallel Similarity Search in High Dimensional Metric Space Using M-Tree

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Advanced Environments, Tools, and Applications for Cluster Computing (IWCC 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2326))

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Abstract

In this study, parallel implementation of M-tree to index high dimensional metric space has been elaborated and an optimal declustering technique has been proposed. First, we have defined the optimal declustering and developed an algorithm based on this definition. Proposed declustering algorithm considers both object proximity and data load on disk/processors by executing a k-NN or a range query for each newly inserted objects. We have tested our algorithm in a database containing randomly chosen 1000 image’s color histograms with 32 bins in HSV color space. Experimentation showed that our algorithm produces a very near optimal declustering.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Dokuz Eylul University, 35100, Bornova, Izmir, Turkey

    Adil Alpkocak, Taner Danisman & Ulker Tuba

Authors
  1. Adil Alpkocak
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  2. Taner Danisman
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  3. Ulker Tuba
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Editor information

Editors and Affiliations

  1. Computer Science Department, University College Cork, College Road, Cork, Ireland

    Dan Grigoras

  2. Information and Computer Science Department, University of California, 444 Computer Science Building, Irvine, CA, 92697-3425, USA

    Alex Nicolau

  3. Laboratoire d’Informatique Fondamentale de Lille (LIFL), University Lille 1, 59655, Villeneuve d’Ascq, France

    Bernard Toursel

  4. LIP, University Paris 6, 8, rue du Capitaine Scott, 75015, Paris, France

    Bertil Folliot

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© 2002 Springer-Verlag Berlin Heidelberg

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Alpkocak, A., Danisman, T., Tuba, U. (2002). A Parallel Similarity Search in High Dimensional Metric Space Using M-Tree. In: Grigoras, D., Nicolau, A., Toursel, B., Folliot, B. (eds) Advanced Environments, Tools, and Applications for Cluster Computing. IWCC 2001. Lecture Notes in Computer Science, vol 2326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47840-X_16

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  • DOI: https://doi.org/10.1007/3-540-47840-X_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43672-0

  • Online ISBN: 978-3-540-47840-9

  • eBook Packages: Springer Book Archive

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