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.
Author(s): S. Sasikala, D. Renuka Devi, Raghvendra Kumar
Publisher: CRC Press/Apple Academic Press
Year: 2023
Language: English
Pages: 309
City: Palm Bay
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
