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High-performance knowledge extraction from data on PC-based networks of workstations

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Parallel and Distributed Processing (IPPS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1586))

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Abstract

The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to Artificial Intelligence, including Data Mining. In this paper we present G-Net, a distributed algorithm able to infer classifiers from pre-collected data, and its implementation on PC-based Networks of Workstations (PC-NOWs). In order to effectively exploit the computing power provided by PC-NOWs, G-Net incorporates a set of dynamic load distribution techniques that allow it to adapt its behavior to variations in the computing power due to resource contention. Moreover, it is provided with a fault tolerance scheme that enables it to continue its computation even if the majority of the machines become unavailable during its execution.

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References

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Author information

Authors and Affiliations

  1. DSTA, Università del Piemonte Orientale, Italy

    Cosimo Anglano & Attilio Giordana

  2. Data Mining Laboratory, CSELT, Torino, Italy

    Giuseppe Lo Bello

Authors
  1. Cosimo Anglano
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  2. Attilio Giordana
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  3. Giuseppe Lo Bello
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Corresponding author

Correspondence to Cosimo Anglano .

Editor information

José Rolim Frank Mueller Albert Y. Zomaya Fikret Ercal Stephan Olariu Binoy Ravindran Jan Gustafsson Hiroaki Takada Ron Olsson Laxmikant V. Kale Pete Beckman Matthew Haines Hossam ElGindy Denis Caromel Serge Chaumette Geoffrey Fox Yi Pan Keqin Li Tao Yang G. Chiola G. Conte L. V. Mancini Domenique Méry Beverly Sanders Devesh Bhatt Viktor Prasanna

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© 1999 Springer-Verlag

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Anglano, C., Giordana, A., Bello, G.L. (1999). High-performance knowledge extraction from data on PC-based networks of workstations. In: Rolim, J., et al. Parallel and Distributed Processing. IPPS 1999. Lecture Notes in Computer Science, vol 1586. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0097998

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  • DOI: https://doi.org/10.1007/BFb0097998

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65831-3

  • Online ISBN: 978-3-540-48932-0

  • eBook Packages: Springer Book Archive

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