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Big Data and Networks Technologies 2020 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 372 pages, height x width: 235x155 mm, weight: 581 g, 120 Illustrations, color; 61 Illustrations, black and white; VII, 372 p. 181 illus., 120 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 81
  • Izdošanas datums: 18-Jul-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030236714
  • ISBN-13: 9783030236717
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 162,93 €*
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  • Standarta cena: 191,69 €
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  • Formāts: Paperback / softback, 372 pages, height x width: 235x155 mm, weight: 581 g, 120 Illustrations, color; 61 Illustrations, black and white; VII, 372 p. 181 illus., 120 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 81
  • Izdošanas datums: 18-Jul-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030236714
  • ISBN-13: 9783030236717
Citas grāmatas par šo tēmu:

This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular.

Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies.