Items related to Big-Data Analytics for Cloud, IoT and Cognitive Computing
Synopsis
The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies
The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming.
Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools.
- The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies
- Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs
- Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies
- Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning
- Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT
Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.
"synopsis" may belong to another edition of this title.
About the Author
Kai Hwang, PhD is Professor of Electrical Engineering and Computer Science at University of Southern California, USA. He also serves as an EMC-endowed visiting Chair Professor at Tsinghua University, China. He specializes in computer architecture, wireless Internet, cloud computing and network security.
Min Chen, PhD is Professor of Computer Science and Technology, Huazhong University of Science and Technology, China. His work focuses on IoT, mobile cloud, body area networks, healthcare big-data and cyber physical systems.
"About this title" may belong to another edition of this title.
- PublisherWiley
- Publication date2017
- ISBN 10 1119247020
- ISBN 13 9781119247029
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages432
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Hardcover. Condition: new. Hardcover. The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologiesCovers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programsOffers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologiesFully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learningFeatures a companion website with an instructor manual and PowerPoint slides Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource. The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781119247029
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