Technologies and Applications for Big Data Value

• Corporate Author: SpringerLink (Online service)
• Other Authors: Curry, Edward (Editor), Auer, Sören (Editor), Berre, Arne J. (Editor), Metzger, Andreas (Editor), Perez, Maria S. (Editor), Zillner, Sonja (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2022.
• Edition: 1st ed. 2022.
• Subjects: Data mining.; Big data.; Quantitative research.; Application software.; Expert systems (Computer science).; Data Mining and Knowledge Discovery.; Big Data.; Data Analysis and Big Data.; Computer and Information Systems Applications.; Knowledge Based Systems.
• Online Access: Link to Metadata

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part "Technologies and Methods" contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part "Processes and Applications" details experience reports and lessons from using big data and data-driven approaches in processes and applications.Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.