Название: Big Data, Cloud Computing and IoT: Tools and Applications
Автор: Sita Rani, Pankaj Bhambri, Aman Kataria, Alex Khang, Arun Kumar Sivaraman
Издательство: CRC Press
Год: 2023
Страниц: 259
Язык: английский
Формат: pdf (true)
Размер: 40.1 MB

Cloud computing, the Internet of Things (IoT), and Big Data are three significant technological trends affecting the world's largest corporations. This book discusses big data, cloud computing, and the IoT, with a focus on the benefits and implementation problems. In addition, it examines the many structures and applications pertinent to these disciplines. Also, Big Data, Cloud Computing, and the IoT are proposed as possible study avenues.

The Internet of Things (IoT), Big Data, and Cloud computing are all independent but complementary fields of study. The integration of three technologies provides synergy and a great chance for organizations to reap the enormous benefits of integration. When this combination is properly conceived, built, implemented, and operated, it can unleash a technical force that can propel innovation forward. Big data, the IoT, and the cloud architectures all work together to bring significant economic advantages. In a way, it is a great fit. The IoT captures data in real time. Data management systems benefit from big data’s optimization. Rapid data collection, storage, computation, and dissemination are all features of the cloud. Big data solutions linked with IoT andcloud architecture are the key focus of this book, which is based on appealing business propositions. As a result, the book provides a high- level overview of architecture, solution practices, governance, and the underlying technical approach to developing integrated big data, cloud, and IoT solutions.

The security of critical systems and infrastructure is a serious concern for information and communication technology systems and networks. There are a variety of ways to ensure that messages are coming from trustworthy sources. The contributors cover the most recent research and development in authentication systems, including problems and applications for cloud technologies, the IoT, and big data in this edited reference.

Recent advances in micro- electro andmicro- mechanical system innovation, remote intersections, and computerized devices have enabled the creation of low- cost, multifunctional sensor hubs that are simple to operate, waste little power, and send data wirelessly over short distances. Intelligent sensors, when utilized like an IoT segment, convert a predicted reality factor into a digital information stream that may be communicated to a gateway for further processing.

The book illuminates the IoT, the cloud, and big data, as well as other cutting- edge technologies. This book addresses a variety of contemporary scientific and technical issues, including how to transform the IoT concept into a practical, technically feasible, and financially viable product. Big data and cloud computing are presented as important enablers for the sensing and computation backbone of the IoT.

Artificial Intelligence (AI) skills that are based on Machine Learning (ML) breakthroughs are the next big thing in the software industry. AI encompasses technology for reasoning, problem-solving, planning, and learning, among others. There has been significant interest in the software and services sector for statistical modeling techniques known as machine learning. Bing Search, Cortana virtual assistant, Microsoft Translator, Cognitive Services, and the Azure AI platform are all examples of Microsoft product teams using machine learning to create application suites and platforms such as Microsoft Translator, Microsoft Cognitive Services, Microsoft Translator, and the Azure AI platform. Microsoft has built on its existing AI capabilities while also developing new areas of knowledge across the organization to generate these software solutions. There is a lot of interest in big data analytics and Deep Learning in Data Science. Deep Learning is a strong technique for Big Data analytics when raw data is unlabeled and uncategorized, because it can analyze and learn from vast volumes of unsupervised data. Some of the hardest problems in Big Data analytics, such as extracting complex patterns from vast volumes of data, semantic indexing, tagging, rapid information retrieval, and simplifying discriminative tasks are addressed in this work.

Features:

Informs about cloud computing, IoT and big data, including theoretical foundations and the most recent empirical findings
Provides essential research on the relationship between various technologies and the aggregate influence they have on solving real-world problems
Ideal for academicians, developers, researchers, computer scientists, practitioners, information technology professionals, students, scholars, and engineers exploring research on the incorporation of technological innovations to address contemporary societal challenges






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