Data-Intensive Computing
eBook - PDF

Data-Intensive Computing

Architectures, Algorithms, and Applications

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Data-Intensive Computing

Architectures, Algorithms, and Applications

Book details
Table of contents
Citations

About This Book

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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Yes, you can access Data-Intensive Computing by Ian Gorton,Deborah K. Gracio in PDF and/or ePUB format, as well as other popular books in Computer Science & Databases. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Cambridge University Press
Year
2012
ISBN
9781139785518
Topic
Computer Science
Subtopic
Databases
Index
Computer Science

Table of contents

  1. Cover
  2. Data-Intensive Computing
  3. Title
  4. Copyright
  5. Contents
  6. List of Contributors
  7. 1 Data-Intensive Computing: A Challenge for the 21st Century
  8. 2 Anatomy of Data-Intensive Computing Applications
  9. 3 Hardware Architectures for Data-Intensive Computing Problems: A Case Study for String Matching
  10. 4 Data Management Architectures
  11. 5 Large-Scale Data Management Techniques in Cloud Computing Platforms
  12. 6 Dimension Reduction for Streaming Data
  13. 7 Binary Classification with Support Vector Machines
  14. 8 Beyond MapReduce: New Requirements for Scalable Data Processing
  15. 9 Let the Data Do the Talking: Hypothesis Discovery from Large-Scale Data Sets in Real Time
  16. 10 Data-Intensive Visual Analysis for Cyber-Security
  17. Index