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Modern Big Data Architectures: A Multi-Agent Systems Perspective

  • ISBN 9781119597841
  • Author: Ryzko, Dominik
  • Publ Date: 2020-03-20
  • Edition:
  • Binding: Hardback

$104.35 + GST

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Provides an up-to-date analysis of big data and multi-agent systems


The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics.


This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence-enabling next generation systems to be built by incorporating the best aspects of the field. This book:





Illustrates how data sets are produced and how they can be utilized in various areas of industry and science

Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks

Discusses current and emerging Big Data applications of Artificial Intelligence



Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.
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