Implement adaptive systems for Big Data processing using next-generation concepts

As artificial intelligence technology advances, so does our ability to solve complex problems by analyzing Big Data. Modern Big Data Architectures offers a look into the future of Big Data, underscoring the untapped potential of the multi-agent systems (MAS) view.

Author Dominik Ryżko, an international expert on processing massive datasets using distributed AI, shows how multiple autonomous AI "agents" can work collaboratively to solve Big Data problems too complex, too difficult, or too large for a typical monolithic system. Considering how the MAS paradigm can improve our current approaches to Big Data, this book covers:

• Agent-based (and non-agent-based) methods of producing Big Data sets
• Key Big Data applications, from product recommendations to drug discovery
• Traditional and next-generation Big Data architectures
• Data mining, machine learning, and SQL analytics using the MAS approach
• Big Data and agents in cloud computing, fog computing, and Internet of Things

Researchers and practitioners alike in the fields of Big Data, analytics, machine learning, cloud computing, and distributed AI will discover new ideas in Modern Big Data Architectures.

The current generation of data systems relies on two concepts that have only recently risen to prominence: Big Data and Artificial Intelligence. Together, Big Data and AI make possible the complex computational tasks that are essential to every field, industry, and corner of contemporary life. As we continue to demand additional value and greater problem-solving ability from our systems, we must find additional ways of conceiving of data processing. New insights are needed to launch a new generation of processing and analytics.

In Modern Big Data Architectures, data scientist Dominik Ryżko presents a view of Big Data architectures that has the potential to expand our thinking on intelligent systems. In the context of a comprehensive overview of Big Data applications covering everything from social media and IoT to fog computing and higher-level industrial processing, Ryżko traces the concept of multi-agent systems (MAS) as a potential paradigm for distributed intelligent systems. This unique book is the first to provide practical guidance on Big Data processing alongside an analysis of MAS and its potential to add value.

Although MAS has not yet risen to prominence in the general public, it has already begun to influence how systems architects and data analysts solve problems using Big Data. A MAS consists of multiple "agents"--autonomous intelligent entities capable of learning and completing specific tasks. When several such agents, each designed with its own specialty, collaborate to solve complex problems, the resulting MAS is capable of exhilarating feats. Big Data and machine learning specialists are already using similar technologies. With this book, readers can fully integrate the power of MAS in their Big Data applications, for more powerful, adaptive, and intelligent systems.

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.