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This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others. Topics include real-time applications in the current big data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; machine learning in OpenFlow Network; and application of deep learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.
Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures
This book discusses the application of data systems and data-driven infrastructure in existing industrial systems in order to optimize workflow, utilize hidden potential, and make existing systems free from vulnerabilities. The book discusses application of data in the health sector, public transportation, the financial institutions, and in battling natural disasters, among others. Topics include real-time applications in the current big data perspective; improving security in IoT devices; data backup techniques for systems; artificial intelligence-based outlier prediction; machine learning in OpenFlow Network; and application of deep learning in blockchain enabled applications. This book is intended for a variety of readers from professional industries, organizations, and students.
The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.
"This book focuses on the challenges of distributed systems imposed by the data intensive applications, and on the different state-of-the-art solutions proposed to overcome these challenges"--Provided by publisher.
META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.
Engineers have long been architects of progress, erecting massive buildings that touch the skies and designing technologies that redefine realms of known possibility. In this era of rapid change, keeping pace with innovation is the main challenge. Emerging Engineering Technologies and Industrial Applications is a comprehensive research book that delves deep into the heart of a relentless technological revolution within modern industry. From the limitless potential of the Internet of Things (IoT) to the cognitive wonders of Artificial Intelligence (AI), from the microscopic marvels of Nanotechnology to the precision and agility of Robotics, and from the revolutionary advancements in 3D Printing to the transformative capabilities of blockchain, this book explores emerging technologies. This book is an indispensable resource for professionals and researchers working in the field of emerging engineering technologies and their industrial applications. It caters to a diverse range of disciplines, including computer science and information technology, environmental, agricultural, and physical sciences, medical, healthcare, and life sciences, as well as science and engineering, and anyone seeking to navigate the current technological progress in the 21st century.
Offering a holistic view of the pioneering trends and innovations in smart healthcare management, this book focuses on the methodologies, frameworks, design issues, tools, architectures, and technologies necessary to develop and understand intelligent healthcare systems and emerging applications in the present era. Smart Technologies in Healthcare Management: Pioneering Trends and Applications provides an overview of various technical and innovative aspects, challenges, and issues in smart healthcare, along with recent and novel findings. It highlights the latest advancements and applications in the field of intelligent systems and explores the importance of cloud computing and the design of sensors in an IoT system. The book offers algorithms and a framework with models in machine learning and AI for smart healthcare management. A detailed flow chart and innovative and modified methodologies related to intelligent computing in healthcare are discussed, as well as real-world-based examples so that readers can compare technical concepts with daily life concepts. This book will be a useful reference for academicians and the healthcare industry, along with professionals interested in exploring innovations in varied applicational areas of AI, IoT, and machine learning. Researchers, startup companies, and entrepreneurs will also find this book of interest.
The advent of 6G technology introduces significant security challenges that must be addressed to ensure its safe and effective implementation. The increased complexity of 6G infrastructure, encompassing a vast array of devices and networks, expands the potential attack surface, making it more vulnerable to cyber threats. Privacy concerns are heightened with the massive data flow, necessitating stringent protection measures. These rapid developments are outpacing current educational frameworks, highlighting the need for updated programs to equip cybersecurity professionals with the skills to address these challenges. 6G Security Education and Multidisciplinary Implementation explores the critical intersection of technology, security, and education. It provides insights into the implementation of 6G technologies as well as frameworks for security education. Covering topics such as 6G education, learning experience, and privacy concerns, this book is a valuable resource for educators, academicians, scholars, security experts, post-graduate students, pre-service teachers, industry professionals, and researchers.
Businesses are facing an unprecedented challenge - the urgent need to adapt and thrive in a world where intelligent factories and supply chains are the new norm. The digital transformation of supply chains is essential for staying competitive, but it is a complex journey fraught with uncertainties. How can organizations harness the power of emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to increase profitability, cut supply chain costs, elevate customer service, and optimize their networks? The answers to these questions are crucial for business survival and success. AI and Machine Learning Impacts in Intelligent Supply Chain is a groundbreaking book that offers a comprehensive solution to the challenges posed by the Industry 4.0 revolution. This book is your indispensable guide to navigating the intricate world of supply chain digital transformation using innovative technologies. It provides real-world examples and insights that illustrate how AI and ML are the keys to solving complex supply chain problems, from inventory management to route optimization and beyond. Whether you are an academic scholar seeking to delve into the impact of AI and ML on supply chain management or a business leader striving to gain a competitive edge, this book is tailored to meet your needs.