Big Data

Principles and Paradigms

Nonfiction, Computers, Database Management, Data Processing, General Computing
Cover of the book Big Data by , Elsevier Science
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Author: ISBN: 9780128093467
Publisher: Elsevier Science Publication: June 7, 2016
Imprint: Morgan Kaufmann Language: English
Author:
ISBN: 9780128093467
Publisher: Elsevier Science
Publication: June 7, 2016
Imprint: Morgan Kaufmann
Language: English

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.

To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

  • Covers computational platforms supporting Big Data applications
  • Addresses key principles underlying Big Data computing
  • Examines key developments supporting next generation Big Data platforms
  • Explores the challenges in Big Data computing and ways to overcome them
  • Contains expert contributors from both academia and industry
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications.

To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

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