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Big Data
Big Data
Knygos.lt klubas Knygos.lt nariams
135,37 €
-30%
Įprastai
193,39 €
  • Išsiųsime per 12–18 d.d.
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 mana…
  • Leidėjas:
  • ISBN-10: 0128053941
  • ISBN-13: 9780128053942
  • Formatas: 19.1 x 23.5 x 2.5 cm, minkšti viršeliai
  • Kalba: Anglų

Big Data (el. knyga) (skaityta knyga) | knygos.lt

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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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  • Leidėjas:
  • ISBN-10: 0128053941
  • ISBN-13: 9780128053942
  • Formatas: 19.1 x 23.5 x 2.5 cm, minkšti viršeliai
  • Kalba: Anglų

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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