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Big-Data Analytics for Cloud, IoT and Cognitive Computing

Book Description

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. 

Table of Contents

  1. About the Authors
  2. Preface
    1. Motivations and Objectives
    2. A Quick Glance of the Book
    3. Our Unique Approach
    4. Building Cloud/IoT Platforms with AI Capabilities
    5. Intended Audience and Readers Guide
    6. Instructor Guide
  3. About the Companion Website
  4. Part 1 Big Data, Clouds and Internet of Things
    1. 1 Big Data Science and Machine Intelligence
      1. 1.1 Enabling Technologies for Big Data Computing
      2. 1.2 Social-Media, Mobile Networks and Cloud Computing
      3. 1.3 Big Data Acquisition and Analytics Evolution
      4. 1.4 Machine Intelligence and Big Data Applications
      5. 1.5 Conclusions
      6. Homework Problems
      7. References
    2. 2 Smart Clouds, Virtualization and Mashup Services
      1. 2.1 Cloud Computing Models and Services
      2. 2.2 Creation of Virtual Machines and Docker Containers
      3. 2.3 Cloud Architectures and Resources Management
      4. 2.4 Case Studies of IaaS, PaaS and SaaS Clouds
      5. 2.5 Mobile Clouds and Inter-Cloud Mashup Services
      6. 2.6 Conclusions
      7. Homework Problems
      8. References
    3. 3 IoT Sensing, Mobile and Cognitive Systems
      1. 3.1 Sensing Technologies for Internet of Things
      2. 3.2 IoT Interactions with GPS, Clouds and Smart Machines
      3. 3.3 Radio Frequency Identification (RFID)
      4. 3.4 Sensors, Wireless Sensor Networks and GPS Systems
      5. 3.5 Cognitive Computing Technologies and Prototype Systems
      6. 3.6 Conclusions
      7. Homework Problems
      8. References
  5. Part 2 Machine Learning and Deep Learning Algorithms
    1. 4 Supervised Machine Learning Algorithms
      1. 4.1 Taxonomy of Machine Learning Algorithms
      2. 4.2 Regression Methods for Machine Learning
      3. 4.3 Supervised Classification Methods
      4. 4.4 Bayesian Network and Ensemble Methods
      5. 4.5 Conclusions
      6. Homework Problems
      7. References
    2. 5 Unsupervised Machine Learning Algorithms
      1. 5.1 Introduction and Association Analysis
      2. 5.2 Clustering Methods without Labels
      3. 5.3 Dimensionality Reduction and Other Algorithms
      4. 5.4 How to Choose Machine Learning Algorithms?
      5. 5.5 Conclusions
      6. Homework Problems
      7. References
    3. 6 Deep Learning with Artificial Neural Networks
      1. 6.1 Introduction
      2. 6.2 Artificial Neural Networks (ANN)
      3. 6.3 Stacked AutoEncoder and Deep Belief Network
      4. 6.4 Convolutional Neural Networks (CNN) and Extensions
      5. 6.5 Conclusions
      6. Homework Problems
      7. References
  6. Part 3 Big Data Analytics for Health-Care and Cognitive Learning
    1. 7 Machine Learning for Big Data in Healthcare Applications
      1. 7.1 Healthcare Problems and Machine Learning Tools
      2. 7.2 IoT-based Healthcare Systems and Applications
      3. 7.3 Big Data Analytics for Healthcare Applications
      4. 7.4 Emotion-Control Healthcare Applications
      5. 7.5 Conclusions
      6. Homework Problems
      7. References
    2. 8 Deep Reinforcement Learning and Social Media Analytics
      1. 8.1 Deep Learning Systems and Social Media Industry
      2. 8.2 Text and Image Recognition using ANN and CNN
      3. 8.3 DeepMind with Deep Reinforcement Learning
      4. 8.4 Data Analytics for Social-Media Applications
      5. 8.5 Conclusions
      6. Homework Problems
      7. References
  7. Index
  8. EULA