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Data-Intensive Computing: Architectures, Algorithms, and Applications

Data-Intensive Computing: Architectures, Algorithms, and Applications

Data-Intensive Computing: Architectures, Algorithms, and Applications
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Data-Intensive Computing: Architectures, Algorithms, and Applications Hardback - 2012

by Gorton, Ian

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Cambridge University Press, 2012-10-29. 1. hardcover. New. 6.00x0.75x9.00. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Data-Intensive Computing: Architectures, Algorithms, and Applications
  • Author Gorton, Ian
  • Binding Hardback
  • Edition 1
  • Condition New
  • Pages 297
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2012-10-29
  • Features Bibliography, Index
  • Bookseller's Inventory # DADAX0521191955
  • ISBN 9780521191951 / 0521191955
  • Weight 1.15 lbs (0.52 kg)
  • Dimensions 9.1 x 6 x 0.8 in (23.11 x 15.24 x 2.03 cm)
  • Size 6.00x0.75x9.00
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Science
  • Library of Congress subjects Database management, Data transmission systems
  • Library of Congress Catalogue Number 2012015720
  • Dewey Decimal Code 004.5
  • Quantity available 1

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From the publisher

The world is awash with digital data from social networks, blogs, business, science, and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms, and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art, and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud, and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

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