Big-Data Analytics for Cloud, IoT and Cognitive Learning. Hwang, K., a. & Chen, M., a. 1st edition.
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 notes = {Kai Hwang, Min Chen.<br/><br/><b><i>Part 1: Big Data, Clouds and Internet of Things<br/></i></b>Chapter 1 Big Data Science and Machine Intelligence<br/>1.1 Enabling Technologies for Big Data Computing<br/>1.1.1 Data Science and Related Disciplines<br/>1.1.2 Emerging Technologies in The Next Decade<br/>1.1.3 Interactive SMACT Technologies<br/>1.2 Social-Media, Mobile Networks and Cloud Computing<br/>1.2.1 Social Networks and Web Service Sites<br/>1.2.2 Mobile Cellular Core Networks<br/>1.2.3 Mobile Devices and Internet Edge Networks<br/>1.2.4 Mobile Cloud Computing Infrastructure<br/>1.3 Big Data Acquisition and Analytics Evolution<br/>1.3.1 Big Data Value Chain Extracted from Massive Data<br/>1.3.2 Data Quality Control<br/>Representation and Database Models<br/>1.3.3 Data Acquisition and Preprocessing<br/>1.3.4 Evolving Data Analytics over The Clouds<br/>1.4 Machine Intelligence and Big Data Applications<br/>1.4.1 Data Mining and Machine Learning<br/>1.4.2 Big Data Applications \2013 An Overview<br/>1.4.3 Cognitive Computing \2013 An Introduction<br/>1.5 Conclusions, References and Exercises<br/>Chapter 2 Smart Clouds<br/>Virtualization and Mashup Services<br/>2.1 Cloud Computing Models and Services<br/>2.1.1 Cloud Taxonomy based on Services Provided<br/>2.1.2 Layered Development Cloud Service Platforms<br/>2.1.3 Cloud Models for Big Data Storage and Processing<br/>2.1.4 Cloud Resources for Supporting Big Data Analytics<br/>2.2 Creation of Virtual Machines and Docker Containers<br/>2.2.1 Virtualization of Machine Resources<br/>2.2.2 Hypervisors and Virtual Machines<br/>2.2.3 Docker Engine and Application Containers<br/>2.2.4 Deployment Opportunity of VMs/Containers<br/>2.3 Cloud Architectures and Resources Management<br/>2.3.1 Cloud Platform Architectures<br/>2.3.2 VM Management and Disaster Recovery<br/>2.3.3 Container Scheduling and Orchestration<br/>2.3.4 OpenStack for Private Cloud Construction<br/>2.3.5 VMWare Packages for Building Hybrid Clouds<br/>2.4 Case Studies of IaaS<br/>PaaS and SaaS Clouds<br/>2.4.1 AWS Architecture over Distributed Datacenters<br/>2.4.2 AWS Cloud Service Offerings<br/>2.4.3 Platform PaaS Clouds \2013 Google App Engine<br/>2.4.4 Application SaaS Clouds \2013 The Salesforce Clouds<br/>2.5 Mobile Clouds and Multi-Cloud Mashup Services<br/>2.5.1 Mobile Clouds and Cloudlet Mesh<br/>2.5.2 Inter-Cloud Mashup Services<br/>2.5.3 Skyline Discovery of Mashup Services<br/>2.5.4 Dynamic Composition of Mashup Services<br/>2.6 Conclusions, References and Home Work<br/>Chapter 3 IoT Sensing, Mobile and Cognitive Systems<br/>3.1  Sensing Technologies for Internet of Things<br/>3.1.1 Enabling Technologies and Evolution of IoT<br/>3.1.2 Introducing RFID and Sensor Technologies<br/>3.1.3 IoT Architecture and Wireless Support<br/>3.2 IoT Interactions with GPS, Clouds and Smart Machines<br/>3.2.1 Local vs. Global Positioning Technologies<br/>3.2.2 Standalone vs.<br/>Cloud-Centric IoT applications<br/>3.2.3 IoT Interaction Frameworks with Environments<br/>3.3 Radio Frequency Identification (RFID)<br/>3.3.1 RFID Technology and Tagging Devices<br/>3.3.2 RFID System Architecture<br/>3.3.3 IoT Support of Supply Chain Management<br/>3.4 Sensors, Wireless Sensor Networks and GPS Systems<br/>3.4.1 Sensor Hardware and Operating Systems<br/>3.4.2 Sensing Through Smart Phones<br/>3.4.3 Wireless Sensor Networks and Body Area Networks<br/>3.4.4 Global Positioning Systems<br/>3.5 Cognitive Computing Technologies and Systems<br/>3.5.1 Cognitive Science and Neuroinformatics<br/>3.5.2 Brain-Inspired Computing Chips and Systems<br/>3.5.3 Google\2019s Brain Team Projects<br/>3.5.4 IoT Contexts for Cognitive Services<br/>3.5.5 Augmented and Virtual Reality Applications<br/>3.6 Conclusions<br/>References and Exercises<br/><b><i>Part 2: Machine Learning and Deep Learning Algorithms<br/></i></b>Chapter 4 Supervised Machine Learning Algorithms<br/>4.1 Taxonomy of Machine Learning Algorithms<br/>4.1.1 Machine Learning based on Learning Styles<br/>4.1.2 Machine Learning based on similarity Testing<br/>4.1.3 Supervised Machine Learning Algorithms<br/>4.1.4 Unsupervised Machine Learning Algorithms<br/>4.2 Regression Methods for Machine Learning<br/>4.2.1 Basic Concept of Regression Analysis<br/>4.2.2 Linear Regression for Prediction or Forecasting<br/>4.2.3 Logistic Regression for Classification<br/>4.3 Supervised Classification Methods<br/>4.3.1 Decision Trees for Machine Learning<br/>4.3.2 Rule-based Classification<br/>4.3.3 Nearest Neighbor Classifier<br/>4.3.4 Support Vector Machines<br/>4.4 Bayesian Network and Ensemble Methods<br/>4.4.1 Bayesian Classifiers<br/>4.4.2 Bayesian Belief Network<br/>4.4.3 Random Forests<br/>and Ensemble Methods<br/>4.5 Conclusions<br/>References and Exercises<br/>Chapter 5 Unsupervised Machine Learning Algorithms<br/>5.1 Introduction and Association Analysis<br/>5.1.1 Introduction To Unsupervised Machine Learning<br/>5.1.2 Association Analysis and Apriori Principle<br/>5.1.3 Association Rule Generation<br/>5.1.4 Case Study of Association Analysis<br/>5.2 Clustering Methods without Labels<br/>5.2.1 Cluster Analysis for Prediction and Forecasting<br/>5.2.2 K-means Clustering for Classification<br/>5.2.3 Agglomerative Hierarchical Clustering<br/>5.2.4 Density-based Clustering<br/>5.3 Dimensionality Reduction and Other Algorithms<br/>5.3.1 Reduce Dimensionality Reduction Methods<br/>5.3.2 Principal Component Analysis (PCA)<br/>5.3.3 Semi-Supervised Machine Learning Methods<br/>5.4 How To Choose Machine Learning Algorithms?<br/>5.4.1 Performance Metrics and Model Fitting<br/>5.4 2 Methods To Reduce Model Overfitting<br/>5.4.3 Methods To Avoid Model<br/>Underfitting<br/>5.4.4 Effects of Using Different Loss Functions<br/>5.5 Conclusions, References and Exercises<br/><b>Chapter 6 Deep Learning with Artificial Neural Networks<br/></b>6.1 Introduction<br/>6.1.1 Deep Learning Mimics Human Senses<br/>6.1.2 Biological versus Artificial Neurons<br/>6.1.3 Deep Learning versus Shallow Learning<br/>6.2 Artificial Neural Networks<br/>6.2.1 Single Layer Artificial Neural Networks<br/>6.2.2 Multilayer Artificial Neural Network (ANN)<br/>6.2.3 Forward Propagation and Back Propagation in ANN<br/>6.3 Stacked Auto-Encoder and Deep Belief Networks<br/>6.3.1 Auto-Encoder<br/>6.3.2 Stacked Auto-Encoder<br/>6.3.3 Restricted Boltzmann Machine<br/>6.3.4 Deep Belief Networks<br/>6.4 Convolutional Neural Networks (CNN) and Extensions<br/>6.4.1 Convolution in CNN<br/>6.4.2 Pooling in CNN<br/>6.4.3 Deep Convolutional Neural Networks<br/>6.4.4 Other Deep Learning Networks<br/>6.5 Conclusions<br/>References and Exercise<br/><b><i>Part 3: Cloud Programming and Analytics Applications<br/></i></b>Chapter 7 Programming with Hadoop, Spark and TensorFlow<br/>7.1 Evolution of Scalable Parallel Computing<br/>7.1.1 Characteristic of Scalable Parallel Computing<br/>7.1.2 From MapReduce to Hadoop and Spark<br/>7.1.3 Software Libraries for Big-Data Cloud Applications<br/>7.2 Hadoop Programming with YARN and HDFS<br/>7.2.1 The MapReduce Computing Engine<br/>7.2.2 MapReduce for Parallel Matrix Multiplication<br/>7.2.3 Hadoop Architecture and Recent Extensions<br/>7.2.4 Hadoop Distributed File System (HDFS)<br/>7.2.5 Hadoop YARN for Resource Management<br/>7.3 Spark Core and Resilient Distributed Datasets<br/>7.3.1 Spark Core for General-Purpose Applications<br/>7.3.2 In-Memory Computation and Language Support<br/>7.3.3 Spark Resilient Distributed Datasets (RDDs)<br/>7.4 Spark SQL, Streaming<br/>Machine Learning and GraphX<br/>7.4.1 Spark SQL with Structured Data<br/>7.4.2 Spark Streaming for Live Stream of Data<br/>7.4.3 Spark MLlib for Mac},
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 bibtype = {book},
 author = {Hwang, Kai author and Chen, Min author}
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