Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development.

Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems.


  • An introduction to data science and the types of data analytics methods accessible today
  • An overview of data integration concepts, methodologies, and solutions
  • A general framework of forecasting principles and applications, as well as basic forecasting models including naïve, moving average, and exponential smoothing models
  • A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies
  • The application of Industry 4.0 and Big Data in the field of education
  • The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance
  • Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making

chapter Chapter 1|38 pages

Data Science and Its Applications

ByPaul Abraham, S. Lakshminarayanan

chapter Chapter 2|16 pages

Industry 4.0: Data and Data Integration

ByPavan Gundarapu

chapter Chapter 3|16 pages

Forecasting Principles and Models: An Overview

ByR. Vijayaraghavan

chapter Chapter 4|13 pages

Breaking Technology Barriers in Diabetes and Industry 4.0

ByKrishnan Swaminathan, Thavamani D. Palaniswami

chapter Chapter 5|21 pages

Role of Big Data Analytics in Industrial Revolution 4.0

ByV. Bhuvaneswari

chapter Chapter 6|17 pages

Big Data Infrastructure and Analytics for Education 4.0

ByE. Chandra, Rathinaraja Jeyaraj

chapter Chapter 7|19 pages

Text Analytics in Big Data Environments

ByR. Janani, S. Vijayarani

chapter Chapter 8|23 pages

Business Data Analytics: Applications and Research Trends

ByS. Sharmila, S. Vijayarani

chapter Chapter 9|25 pages

Role of Big Data Analytics in the Financial Service Sector

ByV. Ramanujam, D. Napoleon

chapter Chapter 10|21 pages

Role of Big Data Analytics in the Education Domain

ByC. Sivamathi, S. Vijayarani

chapter Chapter 11|16 pages

Social Media Analytics

ByE. Suganya, S. Vijayarani

chapter Chapter 12|25 pages

Robust Statistics: Methods and Applications

ByR. Muthukrishnan

chapter Chapter 13|37 pages

Big Data in Tribal Healthcare and Biomedical Research

ByV. Dhivya, V.G. Abilash, Narayanasamy Arul, Chhakchhuak Lalchhandama, V. Balachandar, N. Senthil Kumar

chapter Chapter 14|34 pages

PySpark toward Data Analytics

ByJ. Ramsingh

chapter Chapter 15|18 pages

How to Implement Data Lake for Large Enterprises

ByRagavendran Chandrasekaran

chapter Chapter 17|42 pages

Big Data Analytics: A Text Mining Perspective and Applications in Biomedicine and Healthcare

ByJeyakumar Natarajan, Balu Bhasuran, Gurusamy Murugesan