Research Practitioner's Handbook on Big Data Analytics

This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book�€�s authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media. Addresses the need for a comprehensive resource on the core concepts of big data analytics along with the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. Cover 1 Half Title 2 Title Page 4 Copyright Page 5 About the Authors 6 About the Editor 8 Table of Contents 10 Abbreviations 14 Preface 16 Introduction 18 1. Introduction to Big Data Analytics 20 Abstract 20 1.1 Introduction 20 1.2 Wider Variety of Data 22 1.3 Types and Sources of Big Data 23 1.4 Characteristics of Big Data 28 1.5 Data Property Types 35 1.6 Big Data Analytics 37 1.7 Big Data Analytics Tools with Their Key Features 44 1.8 Techniques of Big Data Analysis 51 Keywords 61 References 61 2. Preprocessing Methods 64 Abstract 64 2.1 Data Mining—Need of Preprocessing 64 2.2 Preprocessing Methods 68 2.3 Challenges of Big Data Streams in Preprocessing 78 2.4 Preprocessing Methods 79 Keywords 87 References 87 3. Feature Selection Methods and Algorithms 90 Abstract 90 3.1 Feature Selection Methods 90 3.2 Types of Fs 91 3.3 Online Fs Methods 97 3.4 Swarm Intelligence in Big Data Analytics 98 3.5 Particle Swarm Optimization 105 3.6 Bat Algorithm 105 3.7 Genetic Algorithms 108 3.8 Ant Colony Optimization 110 3.9 Artificial Bee Colony Algorithm 115 3.10 Cuckoo Search Algorithm 118 3.11 Firefly Algorithm 119 3.12 Grey Wolf Optimization Algorithm 122 3.13 Dragonfly Algorithm 123 3.14 Whale Optimization Algorithm 127 Keywords 128 References 128 4. Big Data Streams 132 Abstract 132 4.1 Introduction 132 4.2 Stream Processing 133 4.3 Benefits of Stream Processing 137 4.4 Streaming Analytics 138 4.5 Real-Time Big Data Processing Life Cycle 138 4.6 Streaming Data Architecture 141 4.7 Modern Streaming Architecture 147 4.8 The Future of Streaming Data in 2019 and Beyond 148 4.9 Big Data and Stream Processing 149 4.10 Framework for Parallelization on Big Data 149 4.11 Hadoop 153 Keywords 172 References 173 5. Big Data Classification 176 Abstract 176 5.1 Classification of Big Data and its Challenges 176 5.2 Machine Learning 178 5.3 Incremental Learning for Big Data Streams 198 5.4 Ensemble Algorithms 199 5.5 Deep Learning Algorithms 207 5.6 Deep Neural Networks 210 5.7 Categories of Deep Learning Algorithms 221 5.8 Application of Dl-Big Data Research 231 Keywords 234 References 234 6. Case Study 236 6.1 Introduction 236 6.2 Healthcare Analytics—Overview 238 6.3 Big Data Analytics Healthcare Systems 250 6.4 Healthcare Companies Implementing Analytics 259 6.5 Social Big Data Analytics 262 6.6 Big Data in Business 274 6.7 Educational Data Analytics 288 Keywords 299 References 299 Index 302 Algorithms;,Database;,Knowledge,Discovery;,Data,Mining Algorithms,Database,Knowledge Discovery,Data Mining "With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches. The book’s authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media. Research Practitioner’s Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics."-- Provided by publisher Throughout the learning behavior among academicians, researchers, and students around the globe, we observe unprecedented interest in big data analytics. As decades pass by, big data analytics knowledge transfer groups have been intensively working on shaping various nuances and techniques and delivering them across the country. This would have not been possible without the Midas touch of researchers who have been extensively carrying out research across domains and connecting big data analytics as a part of other evolving technologies. This book discusses major contributions and perspectives in terms of research over big data and how these concepts serve global markets (IT industry) to lay concrete foundations on the same technology. We hope that this book will offer a wider connotation to researchers and academicians in all walks and on par with their ways of using big data analytics as a theoretical and practical style.