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Big Data: Principles and Paradigms

Editat de Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi
en Limba Engleză Paperback – 9 iun 2016

"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 applicationsAddresses key principles underlying Big Data computingExamines key developments supporting next generation Big Data platformsExplores the challenges in Big Data computing and ways to overcome themContains expert contributors from both academia and industry"

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Specificații

ISBN-13: 9780128053942
ISBN-10: 0128053941
Pagini: 494
Dimensiuni: 191 x 234 x 25 mm
Greutate: 0.84 kg
Editura: ELSEVIER SCIENCE

Cuprins

Part I: Big Data Science
Part II: Big Data Infrastructures and Platforms
Part III: Big Data Security and Policy
Part IV: Big Data Applications

Descriere

"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 applicationsAddresses key principles underlying Big Data computingExamines key developments supporting next generation Big Data platformsExplores the challenges in Big Data computing and ways to overcome themContains expert contributors from both academia and industry"