Data-intensive computing : architectures, algorithms, and applications

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書誌事項

Data-intensive computing : architectures, algorithms, and applications

edited by Ian Gorton, Deborah K. Gracio

Cambridge University Press, 2013

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

  • 1. Data-intensive computing: a challenge for the twenty-first century Ian Gorton and Deborah K. Gracio
  • 2. The anatomy of data-intensive computing applications Ian Gorton and Deborah K. Gracio
  • 3. Hardware architectures for data-intensive computing problems: a case study for string matching Antonino Tumeo, Oreste Villa and Daniel Chavarria-Miranda
  • 4. Data management architectures Terence Critchlow, Ghaleb Abdulla, Jacek Becla, Kerstin Kleese-Van Dam, Sam Lang and Deborah L. McGuinness
  • 5. Large-scale data management techniques in cloud computing platforms Sherif Sakr and Anna Liu
  • 6. Dimension reduction for streaming data Chandrika Kamath
  • 7. Binary classification with support vector machines Patrick Nichols, Bobbie-Jo Webb-Robertson and Christopher Oehmen
  • 8. Beyond MapReduce: new requirements for scalable data processing Bill Howe
  • 9. Letting the data do the talking: hypothesis discovery from large-scale data sets in real time Christopher Oehmen, Scott Dowson, Wes Hatley, Justin Almquist, Bobbie-Jo Webb-Robertson, Jason McDermott, Ian Gorton and Lee Ann McCue
  • 10. Data-intensive visual analysis for cybersecurity William A. Pike, Daniel M. Best, Douglas V. Love and Shawn J. Bohn.

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