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ABOUT THE BOOK

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.

TABLE OF CONTENTS

1. Introduction to Big Data Analytics

Introduction

A Wider Variety of Data

Types and Sources of Big Data

Characteristics of Big Data

Data Property Types

Big Data Analytics

Big Data Analytics Tools with Their Key Features

Techniques of Big Data Analysis

2. Pre-Processing Methods

Data Mining: Need for Preprocessing

Pre-Processing Methods

Challenges of Big Data Streams in Preprocessing

Pre-Processing Methods

3. Feature Selection Methods and Algorithms

Feature Selection Methods

Types of Fs

Swarm Intelligence in Big Data Analytics

Particle Swarm Optimization (PSO)

Bat Algorithm (BA)

Genetic Algorithms

Ant Colony Optimization (ACO)

Artificial Bee Colony Algorithm (ABC)

Cuckoo Search Algorithm

Firefly Algorithm

Grey Wolf Optimization Algorithm (GWO)

Dragonfly Algorithm (DA)

Whale Optimization Algorithm (WOA)

4. Big Data Streams

Introduction

Stream Processing

Benefits of Stream Processing

Streaming Analytics

Real-Time Big Data Processing Lifecycle

Streaming Data Architecture

Modern Streaming Architecture

The Future of Streaming Data in 2021 and Beyond

Big Data and Stream Processing

Framework for Parallelization on Big Data

5. Big Data Classification

Classification in Big Data and Challenges

Machine Learning (ML)

Incremental Learning for Big Data Streams

Ensemble Algorithms

Deep Learning Algorithms

Deep Neural Networks

Categories of Deep Learning Algorithms

6. Case Studies

Introduction

Health Care Analytics: Overview

Big Data Analytics Health Care Systems

Healthcare Companies Implementing Analytics

Social Big Data Analytics

Big Data in Business

Educational Data Analytics