Research Practitioner's Handbook on Big Data Analytics

Description

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. Cover Half Title Title Page Copyright Page About the Authors About the Editor Table of Contents Abbreviations Preface Introduction 1. Introduction to Big Data Analytics Abstract 1.1 Introduction 1.2 Wider Variety of Data 1.3 Types and Sources of Big Data 1.4 Characteristics of Big Data 1.5 Data Property Types 1.6 Big Data Analytics 1.7 Big Data Analytics Tools with Their Key Features 1.8 Techniques of Big Data Analysis Keywords References 2. Preprocessing Methods Abstract 2.1 Data Mining—Need of Preprocessing 2.2 Preprocessing Methods 2.3 Challenges of Big Data Streams in Preprocessing 2.4 Preprocessing Methods Keywords References 3. Feature Selection Methods and Algorithms Abstract 3.1 Feature Selection Methods 3.2 Types of Fs 3.3 Online Fs Methods 3.4 Swarm Intelligence in Big Data Analytics 3.5 Particle Swarm Optimization 3.6 Bat Algorithm 3.7 Genetic Algorithms 3.8 Ant Colony Optimization 3.9 Artificial Bee Colony Algorithm 3.10 Cuckoo Search Algorithm 3.11 Firefly Algorithm 3.12 Grey Wolf Optimization Algorithm 3.13 Dragonfly Algorithm 3.14 Whale Optimization Algorithm Keywords References 4. Big Data Streams Abstract 4.1 Introduction 4.2 Stream Processing 4.3 Benefits of Stream Processing 4.4 Streaming Analytics 4.5 Real-Time Big Data Processing Life Cycle 4.6 Streaming Data Architecture 4.7 Modern Streaming Architecture 4.8 The Future of Streaming Data in 2019 and Beyond 4.9 Big Data and Stream Processing 4.10 Framework for Parallelization on Big Data 4.11 Hadoop Keywords References 5. Big Data Classification Abstract 5.1 Classification of Big Data and its Challenges 5.2 Machine Learning 5.3 Incremental Learning for Big Data Streams 5.4 Ensemble Algorithms 5.5 Deep Learning Algorithms 5.6 Deep Neural Networks 5.7 Categories of Deep Learning Algorithms 5.8 Application of Dl-Big Data Research Keywords References 6. Case Study 6.1 Introduction 6.2 Healthcare Analytics—Overview 6.3 Big Data Analytics Healthcare Systems 6.4 Healthcare Companies Implementing Analytics 6.5 Social Big Data Analytics 6.6 Big Data in Business 6.7 Educational Data Analytics Keywords References Index