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This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.
Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.
This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.
Cloud Computing in Ocean and Atmospheric Sciences provides the latest information on this relatively new platform for scientific computing, which has great possibilities and challenges, including pricing and deployments costs and applications that are often presented as primarily business oriented. In addition, scientific users may be very familiar with these types of models and applications, but relatively unfamiliar with the intricacies of the hardware platforms they use. The book provides a range of practical examples of cloud applications that are written to be accessible to practitioners, researchers, and students in affiliated fields. By providing general information on the use of the cloud for oceanographic and atmospheric computing, as well as examples of specific applications, this book encourages and educates potential users of the cloud. The chapters provide an introduction to the practical aspects of deploying in the cloud, also providing examples of workflows and techniques that can be reused in new projects. - Provides real examples that help new users quickly understand the cloud and provide guidance for new projects - Presents proof of the usability of the techniques and a clear path to adoption of the techniques by other researchers - Includes real research and development examples - that are ideal for cloud computing adopters in ocean and atmospheric domains
This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.
This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy.
Agriculture plays a vital role in a country’s growth. Modern-day technologies drive every domain toward smart systems. The use of traditional agricultural procedures to satisfy modern-day requirements is a challenging task. Cloud IoT Systems for Smart Agricultural Engineering provides substantial coverage of various challenges of the agriculture domain through modern technologies such as the Internet of Things (IoT), cloud computing, and many more. This book offers various state-of-the-art procedures to be deployed in a wide range of agricultural activities. The concepts are discussed with the necessary implementations and clear examples. Necessary illustrations are depicted in the chapters to ensure the effective delivery of the proposed concepts. It presents the rapid advancement of the technologies in the existing agricultural model by applying the cloud IoT techniques. A wide variety of novel architectural solutions are discussed in various chapters of this book. This book provides comprehensive coverage of the most essential topics, including: New approaches on urban and vertical farming Smart crop management for Indian farmers Smart livestock management Precision agriculture using geographical information systems Machine learning techniques combined with IoT for smart agriculture Effective use of drones in smart agriculture This book provides solutions for the diverse domain of problems in agricultural engineering. It can be used at the basic and intermediary levels for agricultural science and engineering graduate students, researchers, and practitioners.
Cloud-based Intelligent Informative Engineering for Society 5.0 is a model for the dissemination of cutting-edge technological innovation and assistive devices for people with physical impairments. This book showcases Cloud-based, high-performance Information systems and Informatics-based solutions for the verification of the information support requirements of the modern engineering, healthcare, modern business, organization, and academic communities. Features: Includes broad variety of methodologies and technical developments to improve research in informative engineering Explore the Internet of Things (IoT), blockchain technology, deep learning, data analytics, and cloud Highlight Cloud-based high-performance Information systems and Informatics-based solutions This book is beneficial for graduate students and researchers in computer sciences, cloud computing and related subject areas.
Recently, several fog computing applications have been developed like IoT-based healthcare, 5G, blockchains, autonomous driving, and mobile wireless applications. They also address challenges such as data management, scalability, regulations, interoperability, device network human interfaces, security, and privacy. Further study on these applications is required to ensure this technology is utilized appropriately. Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time focuses on fog computing problems and solutions for various applications and covers the new approaches, architecture, and theoretical foundations in the fog paradigm of storage, communication, and computing. The book explores recent trends and challenges that lead to a potential course for the ideas, practices, norms, and strategies related to fog computing. Covering key topics such as data privacy, data analytics, and the internet of things, this reference work is ideal for computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.