Books in the Chapman & Hall/CRC Big Data Series series

The ultimate objective of this book is to provide the academic and industrial communities with an in-depth introduction to the recent advances in development, theory, and impacts of Big Data, tailored to applications in complex and social networks. Specifically, the book focuses on how to retrieve, store, manipulate and analyze data and how to develop new tools and techniques to study and visualize massive datasets in the domain of complex and social networks.

This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It covers fundamental issues in Big Data research, including emerging architectures for data-intensive applications, novel analytical strategies to boost data processing, and cutting-edge applications.

This book will provide a comprehensive overview of recent research and open problems in the area of IoT research. It will cover state of the art problems, present solutions, and open research directions and will be targeted to researchers and scholars in both industry and academia.

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis.

Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. The book's purpose is to provide a detailed overview of big data systems. It adopts a challenge-centric approach in which platforms are evaluated based on their capabilities to solve specific challenges.

Presenting the contributions of leading experts in their respective fields, this book bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues regarding Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in fields such as medicine, science, and engineering. Coverage includes Big Data management, Big Data processing, Big Data streaming techniques and algorithms, Big Data privacy, and Big Data applications.

This book unravels the mystery of Big Data computing and its power to transform business operations. The approach it uses will be helpful to any professional who must present a case for realizing Big Data computing solutions or to those who could be involved in a Big Data computing project. It provides a framework that enables business and technical managers to make optimal decisions necessary for the successful migration to Big Data computing environments and applications within their organizations.

This book aims to bridge the gap between large amounts of data and appropriate computational and management methods for scientific discovery. It explores technologies for media/data communication, elastic media/data storage, and cross-network media/data fusion.

This book presents state-of-the-art research, methodologies, and applications of high performance computing for big data applications. It covers fundamental issues in Big Data research, including emerging architectures for data-intensive applications, novel analytical strategies to boost data processing, and cutting-edge applications.

This book aims to bridge the gap between large amounts of data and appropriate computational and management methods for scientific discovery. It explores technologies for media/data communication, elastic media/data storage, and cross-network media/data fusion.

Presenting the contributions of leading experts in their respective fields, this book bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues regarding Big Data, including efficient algorithmic methods to process data, better analytical strategie