Big Data Analytics: Systems, Algorithms, Applications

by C.S.R. Prabhu

Table of Preface Chapter 1 Big Data 1.1 Introduction 1.2 What is Big Data? 1.3 Disruptive change and paradigm shift and its business meaning 1.4 HADOOP 1.4.1 Silos 1.4.2 Big Bang of Big Data 1.4.3 Possibilities 1.4.4 Future 1.4.5 Parallel processing for problem solving 1.4.6 Why Hadoop? 1.4.7 Hadoop and HDFS 1.4.8 Hadoop Version 1.0 & 2.0 1.4.8.1 Limitations of Hadoop 1.0 1.4.9 Hadoop 2.0 1.5 HDFS Overview 1.5.1 Map Reduce framework 1.5.2 Job Tracker 1.5.3 YARN 1.6 Hadoop Eco System 1.6.1 Cloud based Hadoop Solutions 1.6.2 SPARK and Data Stream Processing 1.7 Decision Making and Data Analysis in the Context of Big Data Environment 1.7.1 Present Day Data Analytics Techniques 1.8 Machine Learning Algorithms 1.9 Evolutionary Computing (EC) Conclusion Review Questions References Chapter 2: Intelligent Systems 2.1 Introduction 2.2 Machine Learning Paradigms 2.2.1 Open Source Data Science 2.2.2 Machine Intelligence and Computational Intelligence 2.2.3 Data Engineering and Data Sciences 2.3 Machine Learning Paradigms 2.4 Big Data Computing 2.4.1 Distributed Systems and Database Systems 2.4.2 Data Stream Systems and Stream Mining 2.4.3 Ubiquitous Computing Infrastructures Conclusion Review Questions References Chapter 3: Predictive Modeling for Unstructured Data 3.1 Introduction 3.2 Applications of Predictive Modeling 3.3 Feature Engineering 3.4 Pattern Mining for Predictive Modeling Conclusion Review Questions References Chapter 4: Machine Learning Algorithms for Big Data 4.1 Introduction 4.2 Generative vs Discriminative Algorithms 4.3 Supervised Learning for Big Data 4.3.1 Decision Trees 4.3.2 Logistic Regression 4.3.3 Regression and Forecasting 4.3.4 Supervised Neural Networks 4.3.5 Support Vector Machines 4.4 Unsupervised Learning for Big Data 4.4.1 Spectral Clustering 4.4.2 Principal Component Analysis (PCA) 4.4.3 Latent Dirichlet Allocation (LDA) 4.4.4 Matrix Factorization 4.4.5 Manifold Learning 4.5 Semi-Supervised Learning for Big Data 4.5.1 Co-training 4.5.2 Label Propagation 4.5.3 Multi-View Learning 4.6 Reinforcement Learning Basics for Big Data 4.6.1 Markov Decision Process 4.6.2

440 pages, Paperback

Published October 17, 2019