Big-Data Analytics for Cloud, IoT and Cognitive Computing

Title:

Personal Author:

Hwang, Kai, author

Physical Description:

xvii, 409 pages : illustrations ; 25 cm.

ISBN:

9781119247029

Abstract:

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

Subject Term:

Cloud computing -- Data processing

Big data

Artificial intelligence

Internet of things

Added Author:

Chen, Min, 1980-,

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010370288	QA76.585 H934 2017	Open Access Book	Book	Searching... Unknown

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Summary

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.

Author Notes

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 the Authors	p. xi
Preface	p. xiii
About the Companion Website	p. xvii
Part 1 Big Data, Clouds and Internet of Things	p. 1
1 Big Data Science and Machine Intelligence	p. 3
1.1 Enabling Technologies for Big Data Computing	p. 3
1.1.1 Data Science and Related Disciplines	p. 4
1.1.2 Emerging Technologies in the Next Decade	p. 7
1.1.3 Interactive SMACT Technologies	p. 13
1.2 Social-Media, Mobile Networks and Cloud Computing	p. 16
1.2.1 Social Networks and Web Service Sites	p. 17
1.2.2 Mobile Cellular Core Networks	p. 19
1.2.3 Mobile Devices and Internet Edge Networks	p. 20
1.2.4 Mobile Cloud Computing Infrastructure	p. 23
1.3 Big Data Acquisition and Analytics Evolution	p. 24
1.3.1 Big Data Value Chain Extracted from Massive Data	p. 24
1.3.2 Data Quality Control, Representation and Database Models	p. 26
1.3.3 Big Data Acquisition and Preprocessing	p. 27
1.3.4 Evolving Data Analytics over the Clouds	p. 30
1.4 Machine Intelligence and Big Data Applications	p. 32
1.4.1 Data Mining and Machine Learning	p. 32
1.4.2 Big Data Applications - An Overview	p. 34
1.4.3 Cognitive Computing - An Introduction	p. 38
1.5 Conclusions	p. 42
Homework Problems	p. 42
References	p. 43
2 Smart Clouds, Visualization and Mashup Services	p. 45
2.1 Cloud Computing Models and Services	p. 45
2.1.1 Cloud Taxonomy based on Services Provided	p. 46
2.1.2 Layered Development Cloud Service Platforms	p. 50
2.1.3 Cloud Models for Big Data Storage and Processing	p. 52
2.1.4 Cloud Resources for Supporting Big Data Analytics	p. 55
2.2 Creation of Virtual Machines and Docker Containers	p. 57
2.2.1 Virtualization of Machine Resources	p. 58
2.2.2 Hypervisors and Virtual Machines	p. 60
2.2.3 Docker Engine and Application Containers	p. 62
2.2.4 Deployment Opportunity of VMs/Containers	p. 64
2.3 Cloud Architectures and Resources Management	p. 65
2.3.1 Cloud Platform Architectures	p. 65
2.3.2 VM Management and Disaster Recovery	p. 68
2.3.3 OpenStack for Constructing Private Clouds	p. 70
2.3.4 Container Scheduling and Orchestration	p. 74
2.3.5 VM Ware Packages for Building Hybrid Clouds	p. 75
2.4 Case Studies of IaaS, PaaS and SaaS Clouds	p. 77
2.4.1 AWS Architecture over Distributed Datacenters	p. 78
2.4.2 AWS Cloud Service Offerings	p. 79
2.4.3 Platform PaaS Clouds - Google AppEngine	p. 83
2.4.4 Application SaaS Clouds - The Salesforce Clouds	p. 86
2.5 Mobile Clouds and Inter-Cloud Mashup Services	p. 88
2.5.1 Mobile Clouds and Cloudlet Gateways	p. 88
2.5.2 Multi-Cloud Mashup Services	p. 91
2.5.3 Skyline Discovery of Mashup Services	p. 95
2.5.4 Dynamic Composition of Mashup Services	p. 96
2.6 Conclusions	p. 98
Homework Problems	p. 98
References	p. 103
3 IoT Sensing, Mobile and Cognitive Systems	p. 105
3.1 Sensing Technologies for Internet of Things	p. 105
3.11 Enabling Technologies and Evolution of IoT	p. 106
3.1.2 Introducing RFID and Sensor Technologies	p. 108
3.1.3 IoT Architectural and Wireless Support	p. 110
3.2 IoT Interactions with GPS, Clouds and Smart Machines	p. 111
3.2.1 Local versus Global Positioning Technologies	p. 111
3.2.2 Standalone versus Cloud-Centric IoT Applications	p. 114
3.2.3 IoT Interaction Frameworks with Environments	p. 116
3.3 Radio Frequency Identification (RFID)	p. 119
3.3.1 RFID Technology and Tagging Devices	p. 119
3.3.2 RFID System Architecture	p. 120
3.3.3 IoT Support of Supply Chain Management	p. 122
3.4 Sensors, Wireless Sensor Networks and GPS Systems	p. 124
3.4.1 Sensor Hardware and Operating Systems	p. 124
3.4.2 Sensing through Smart Phones	p. 130
3.4.3 Wireless Sensor Networks and Body Area Networks	p. 131
3.4.4 Global Positioning Systems	p. 134
3.5 Cognitive Computing Technologies and Prototype Systems	p. 139
3.5.1 Cognitive Science and Neuroinformatics	p. 139
3.5.2 Brain-Inspired Computing Chips and Systems	p. 140
3.5.3 Google's Brain Team Projects	p. 142
3.5.4 IoT Contexts for Cognitive Services	p. 145
3.5.5 Augmented and Virtual Reality Applications	p. 146
3.6 Conclusions	p. 149
Homework Problems	p. 150
References	p. 152
Part 2 Machine Learning and Deep Learning Algorithms	p. 155
4 Supervised Machine Learning Algorithms	p. 157
4.1 Taxonomy of Machine Learning Algorithms	p. 157
4.1.1 Machine Learning Based on Learning Styles	p. 158
4.1.2 Machine Learning Based on Similarity Testing	p. 159
4.1.3 Supervised Machine Learning Algorithms	p. 162
4.1.4 Unsupervised Machine Learning Algorithms	p. 163
4.2 Regression Methods for Machine Learning	p. 164
4.2.1 Basic Concepts of Regression Analysis	p. 164
4.2.2 Linear Regression for Prediction and Forecast	p. 166
4.2.3 Logistic Regression for Classification	p. 169
4.3 Supervised Classification Methods	p. 171
4.3.1 Decision Trees for Machine Learning	p. 171
4.3.2 Rule-based Classification	p. 175
4.3.3 The Nearest Neighbor Classifier	p. 181
4.3.4 Support Vector Machines	p. 183
4.4 Bayesian Network and Ensemble Methods	p. 187
4.4.1 Bayesian Classifiers	p. 188
4.4.2 Bayesian Belief Networks	p. 191
4.4.3 Random Forests and Ensemble Methods	p. 195
4.5 Conclusions	p. 200
Homework Problems	p. 200
References	p. 203
5 Unsupervised Machine Learning Algorithms	p. 205
5.1 Introduction and Association Analysis	p. 205
5.1.1 Introduction to Unsupervised Machine Learning	p. 205
5.1.2 Association Analysis and A priori Principle	p. 206
5.1.3 Association Rule Generation	p. 210
5.2 Clustering Methods without Labels	p. 213
5.2.1 Cluster Analysis for Prediction and Forecasting	p. 213
5.2.2 K-means Clustering for Classification	p. 214
5.2.3 Agglomerative Hierarchical Clustering	p. 217
5.2.4 Density-based Clustering	p. 221
5.3 Dimensionality Reduction and Other Algorithms	p. 225
5.3.1 Dimensionality Reduction Methods	p. 225
5.3.2 Principal Component Analysis (PCA)	p. 226
5.3.3 Semi-Supervised Machine Learning Methods	p. 231
5.4 How to Choose Machine Learning Algorithms?	p. 233
5.4.1 Performance Metrics and Model Fitting	p. 233
5.4.2 Methods to Reduce Model Over-Fitting	p. 237
5.4.3 Methods to Avoid Model Under-Fitting	p. 240
5.4.4 Effects of Using Different Loss Functions	p. 242
5.5 Conclusions	p. 243
Homework Problems	p. 243
References	p. 247
6 Deep Learning with Artificial Neural Networks	p. 249
6.1 Introduction	p. 249
6.1.1 Deep Learning Mimics Human Senses	p. 249
6.1.2 Biological Neurons versus Artificial Neurons	p. 251
6.1.3 Deep Learning versus Shallow Learning	p. 254
6.2 Artificial Neural Networks (ANN)	p. 256
6.2.1 Single Layer Artificial Neural Networks	p. 256
6.2.2 Multilayer Artificial Neural Network	p. 257
6.2.3 Forward Propagation and Back Propagation in ANN	p. 258
6.3 Stacked AutoEncoder and Deep Belief Network	p. 264
6.3.1 AutoEncoder	p. 264
6.3.2 Stacked AutoEncoder	p. 267
6.3.3 Restricted Boltzmann Machine	p. 269
6.3.4 Deep Belief Networks	p. 275
6.4 Convolutional Neural Networks (CNN) and Extensions	p. 277
6.4.1 Convolution in CNN	p. 277
6.4.2 Pooling in CNN	p. 280
6.4.3 Deep Convolutional Neural Networks	p. 282
6.4.4 Other Deep Learning Networks	p. 283
6.5 Conclusions	p. 287
Homework Problems	p. 288
References	p. 291
Part 3 Big Data Analytics for Health-Care and Cognitive Learning	p. 293
7 Machine Learning for Big Data in Healthcare Applications	p. 295
7.1 Healthcare Problems and Machine Learning Tools	p. 295
7.1.1 Healthcare and Chronic Disease Detection Problem	p. 295
7.1.2 Software Libraries for Machine Learning Applications	p. 298
7.2 IoT-based Healthcare Systems and Applications	p. 299
7.2.1 IoT Sensing for Body Signals	p. 300
7.2.2 Healthcare Monitoring System	p. 301
7.2.3 Physical Exercise Promotion and Smart Clothing	p. 304
7.2.4 Healthcare Robotics and Mobile Health Cloud	p. 305
7.3 Big Data Analytics for Healthcare Applications	p. 310
7.3.1 Healthcare Big Data Preprocessing	p. 310
7.3.2 Predictive Analytics for Disease Detection	p. 312
7.3.3 Performance Analysis of Five Disease Detection Methods	p. 316
7.3.4 Mobile Big Data for Disease Control	p. 320
7.4 Emotion-Control Healthcare Applications	p. 322
7.4.1 Mental Healthcare System	p. 323
7.4.2 Emotion - Control Computing and Services	p. 323
7.4.3 Emotion Interaction through IoT and Clouds	p. 327
7.4.4 Emotion-Control via Robotics Technologies	p. 329
7.4.5 A 5G Cloud-Centric Healthcare System	p. 332
7.5 Conclusions	p. 335
Homework Problems	p. 336
References	p. 339
8 Deep Reinforcement Learning and Social Media Analytics	p. 343
8.1 Deep Learning Systems and Social Media Industry	p. 343
5.1.1 Deep Learning Systems and Software Support	p. 343
8.1.1 Reinforcement Learning Principles	p. 346
8.1.2 Social-Media Industry and Global Impact	p. 347
8.2 Text and image Recognition using ANN and CNN	p. 348
8.2.1 Numeral Recognition using Tensor Flow for ANN	p. 349
8.2.2 Numeral Recognition using Convolutional Neural Networks	p. 352
8.2.3 Convolutional Neural Networks for Face Recognition	p. 356
8.2.4 Medical Text Analytics by Convolutional Neural Networks	p. 357
8.3 DeepMind with Deep Reinforcement Learning	p. 362
8.3.1 Google DeepMind AI Programs	p. 362
8.3.2 Deep Reinforcement Learning Algorithm	p. 364
8.3.3 Google AlphaGo Game Competition	p. 367
8.3.4 Flappybird Game using Reinforcement Learning	p. 371
8.4 Data Analytics for Social-Media Applications	p. 375
8.4.1 Big Data Requirements in Social-Media Applications	p. 375
8.4.2 Social Networks and Graph Analytics	p. 377
8.4.3 Predictive Analytics Software Tools	p. 383
8.4.4 Community Detection in Social Networks	p. 386
8.5 Conclusions	p. 390
Homework Problems	p. 391
References	p. 393
Index	p. 395

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Author Notes

Table of Contents