Modern Big Data Architectures: A Multi-Agent Systems Perspective

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.