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Full Description
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.
Features
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
Contents
Preface. Acknowledgments. Editors. Contributors. 1 Data Science and Its Applications. 2 Industry 4.0: Data and Data Integration. 3 Forecasting Principles and Models: An Overview. 4 Breaking Technology Barriers in Diabetes and Industry 4.0. 5 Role of Big Data Analytics in Industrial Revolution 4.0. 6 Big Data Infrastructure and Analytics for Education 4.0. 7 Text Analytics in Big Data Environments. 8 Business Data Analytics: Applications and Research Trends. 9 Role of Big Data Analytics in the Financial Service Sector. 10 Role of Big Data Analytics in the Education Domain. 11 Social Media Analytics. 12 Robust Statistics: Methods and Applications. 13 Big Data in Tribal Healthcare and Biomedical Research. 14 PySpark towards Data Analytics. 15 How to Implement Data Lake for Large Enterprises. 16 A Novel Application of Data Mining Techniques for Satellite Performance Analysis. 17 Big Data Analytics: A Text Mining Perspective and Applications in Biomedicine and Healthcare. Index.
