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Big Data Analytics and Software Defined Networking (SDN) are helping to drive the management of data usage of the extraordinary increase of computer processing power provided by Cloud Data Centres (CDCs). This new book investigates areas where Big-Data and SDN can help each other in delivering more efficient services.

The authors present a comprehensive review of fog computing systems including energy efficiency and quality of service issues, reliability and fault tolerance, load balancing, and scheduling, utilizing mobile edge computing, internet of things, resource estimation and virtualization in fog computing environments for smart applications.

This book provides an overview of multimedia technologies used in finance and banking, including ML and DL techniques for financial data analysis, fraud and cyber operation countermeasures, concrete applications of NPR for financial data, self-supervised, unsupervised & semi-supervised learning methods and real-world case studies.

Using machine and deep learning techniques the authors introduce pre-processing methods applied to satellite images to identify land cover features, detect object, classify crops, recognize targets, and monitor and support earth resources. Readers will need a basic understanding of computing, remote sensing and image interpretation.

In this edited book, the authors present the challenges, technologies, applications and future trends of the artificial intelligence-enabled internet of things (AIoT) in realizing smart and intelligent environments.

This book covers challenges and solutions of autonomic computing trustworthiness from methods and techniques to achieve consistent and reliable system self-management. This is an ideal tutorial guide for independent study with simple and well documented diagrams to explain techniques and processes.

Presenting a comprehensive view of blockchain technologies for network security from principles to core technologies and applications, this book offers unprecedented insights into recent advances and developments in these areas, and how they can make blockchain technologies associated with networks more secure and fit-for-purpose.

This book covers state of the art interdisciplinary research on key disruptive and interrelated technologies such as Big Data, Edge computing, IoT and Cloud computing. The authors address the challenges from a distributed system perspective, with clear contributions in theory and applications.

This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.

This book combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools.

The primary aim of this book is to provide a timely and coherent account of the recent advances in many-core computing research. Starting with programming models, operating systems and their applications; it presents runtime management techniques, followed by system modelling, verification and testing methods, and architectures and systems.

This book describes the primary uses for Trusted Platform Modules (TPMs) and practical considerations such as when TPMs can and should be used, when they shouldn't be, what advantages they provide, and how to actually make use of them, with use cases and worked examples of how to implement these use cases on a real system.

Intellectual Property Rights for Engineers explains the general principles behind the law protecting innovation, quoting cases from the engineering domain in order to clarify legal issues.

This book shows how innovations in network analytics, IoTs and cloud computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance.

Graph analytics are being empowered through novel analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book presents the various aspects and importance of graph data science, with contributions by authors from academia and industry.

In this book, the authors articulate the challenges associated with streaming data and analytics, describe data analytics algorithms and approaches, present edge and fog computing concepts and technologies and show how streaming analytics can be accomplished in edge device clouds. They also delineate several industry use cases.

Inspired by MPEG-I and JPEG-PLENO standardization activities, this book is for readers who want to understand 3D representations and multi-camera video processing for novel immersive media applications. The authors address new challenges that arise beyond compression-only, such as depth acquisition and 3D rendering.

Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) and data-intensive applications, and strategies to map ML designs onto hardware accelerators.

This comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that support Big Data analytics, while the second volume offers a wide range of Big Data analytics applications.

This comprehensive edited 2-volume handbook provides a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics. The first volume presents methodologies that support Big Data analytics, while the second volume offers a wide range of Big Data analytics applications.

This book describes how smart cities can be designed with data at their heart, moving from a broad vision to a consistent city-wide collaborative configuration of activities.

SysML for Systems Engineering: A model-based approach provides a comprehensive overview on how to implement SysML and Model-based Systems Engineering (MBSE) in an organisation in order to model real projects effectively and efficiently.

This book examines various topics and approaches related to the security and privacy in big data and cloud computing, where authors share their expertise in their respective chapters on a broad range of security and privacy challenges and state of the art solutions.