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Big data : principles and paradigms / [edited by] Rajkumar Buyya, Rodrigo N. Calheiros and Amir Vahid Dastjerdi.

Contributor(s): Material type: TextTextPublication details: Amsterdam : Elsevier, 2016.Description: xxv, 468 pages : illustrations ; 24 cmISBN:
  • 9780128053942
Subject(s): DDC classification:
  • 005.7  23 B992
Contents:
Part I. Big data science -- part II. Big data infrastructures and platforms -- part III. Big data security and privacy -- part IV. Big data applications.
Summary: 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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 005.7 B992 (Browse shelf(Opens below)) Available 137832
Total holds: 0

Includes bibliographical references and index.

Part I. Big data science --
part II. Big data infrastructures and platforms --
part III. Big data security and privacy --
part IV. Big data applications.

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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