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

Contributor(s): Buyya, Rajkumar, 1970- [editor.] | Calheiros, Rodrigo N [editor.] | Vahid Dastjerdi, Amir [editor.]Material type: Text

TextPublisher: Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, [2016]Description: 1 online resource (xxv, 468 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 9780128093467; 0128093463Subject(s): Big data | COMPUTERS -- Data Processing | Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Big data.DDC classification: 005.7 LOC classification: QA76.9.B45Online resources: ScienceDirect

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
Ebooks	Mysore University Main Library		Not for loan		EBKELV329

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.

Online resource; title from PDF title page (ScienceDirect, viewed July 11, 2016).

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.

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