Overview
- Explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy
- Provides an overview of technologies and methods which enable data value chains and which can be applied in any sector
- Details experience reports and lessons from using big data and data-driven approaches in processes and applications from key application domains including health, law, finance, retail, manufacturing, mobility, transport, and smart cities
- This book is open access, which means that you have free and unlimited access
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Table of contents (23 chapters)
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Front Matter
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Technologies and Methods
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Front Matter
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Processes and Applications
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Front Matter
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Keywords
About this book
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.
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.
Editors and Affiliations
About the editors
Edward Curry is a research leader at the Insight SFI Research Centre for Data Analytics. He has made contributions to semantic technologies, incremental data management, event processing middleware, software engineering, and distributed systems and information systems. Edward combines strong theoretical results with high-impact practical applications. He is also co-founder and elected Vice President of the Big Data Value Association, an industry-led European big data community.
Sören Auer is Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB, the largest science and technology library in the world. He has made important contributions to semantic technologies, knowledge engineering and information systems. He is co-founder of several high potential research and community projects such as the Wikipedia semantification project DBpedia, the scholarly platform knowledge graphorkg.org and the innovative technology start-up eccenca.com. Sören also was founding director of the Big Data Value Association, led the semantic data representation in the International Data Space, and is an expert for industry, the European Commission and W3C.
Arne J. Berre is Chief Scientist at SINTEF Digital and Innovation Director at the Norwegian Center for AI Innovation (NorwAI), responsible for the GEMINI center of Big Data and AI. He is the leader of the BDVA/DAIRO TF6 on technical priorities including responsibilities for data technology architectures, data science/AI, data protection, standardisation, benchmarking and HPC, as well as the lead of the Norwegian committee for AI and Big Data with ISO SC 42 AI.
Andreas Metzger is senior academic councillor at the University of Duisburg-Essen and heads the Adaptive Systems and Big Data Applications group at paluno, the Ruhr Institute for Software Technology. His background and research interests are software engineering and machine learning for adaptive systems. Among other leadership roles, Andreas acted as Technical Coordinator of the European lighthouse project TransformingTransport, which demonstrated the transformations that big data and machine learning can bring to the mobility and logistics sector.
Maria S. Perez is full professor at the Universidad Politécnica de Madrid (UPM). She is part of the Board of Directors of the Big Data Value Association and also a member of the Research and Innovation Advisory Group of the EuroHPC Joint Undertaking. Her research interests include data science, big data, machine learning, storage, high performance, and large-scale computing.
Sonja Zillner works at Siemens AG Technology as Principal Research Scientist, focusing on the definition, acquisition and management of global innovation and research projects in the domain of semantics and artificial intelligence. Since 2020 she is Lead of Core Company Technology Module “Trustworthy AI” at Siemens Corporate Technology. Before that, from 2016 to 2019 she was invited to consult the Siemens Advisory Board in strategic decisions regarding artificial intelligence. In addition, Sonja is professor at Technical University in Munich
Sören Auer is Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB, the largest science and technology library in the world. He has made important contributions to semantic technologies, knowledge engineering and information systems. He is co-founder of several high potential research and community projects such as the Wikipedia semantification project DBpedia, the scholarly platform knowledge graphorkg.org and the innovative technology start-up eccenca.com. Sören also was founding director of the Big Data Value Association, led the semantic data representation in the International Data Space, and is an expert for industry, the European Commission and W3C.
Arne J. Berre is Chief Scientist at SINTEF Digital and Innovation Director at the Norwegian Center for AI Innovation (NorwAI), responsible for the GEMINI center of Big Data and AI. He is the leader of the BDVA/DAIRO TF6 on technical priorities including responsibilities for data technology architectures, data science/AI, data protection, standardisation, benchmarking and HPC, as well as the lead of the Norwegian committee for AI and Big Data with ISO SC 42 AI.
Andreas Metzger is senior academic councillor at the University of Duisburg-Essen and heads the Adaptive Systems and Big Data Applications group at paluno, the Ruhr Institute for Software Technology. His background and research interests are software engineering and machine learning for adaptive systems. Among other leadership roles, Andreas acted as Technical Coordinator of the European lighthouse project TransformingTransport, which demonstrated the transformations that big data and machine learning can bring to the mobility and logistics sector.
Maria S. Perez is full professor at the Universidad Politécnica de Madrid (UPM). She is part of the Board of Directors of the Big Data Value Association and also a member of the Research and Innovation Advisory Group of the EuroHPC Joint Undertaking. Her research interests include data science, big data, machine learning, storage, high performance, and large-scale computing.
Sonja Zillner works at Siemens AG Technology as Principal Research Scientist, focusing on the definition, acquisition and management of global innovation and research projects in the domain of semantics and artificial intelligence. Since 2020 she is Lead of Core Company Technology Module “Trustworthy AI” at Siemens Corporate Technology. Before that, from 2016 to 2019 she was invited to consult the Siemens Advisory Board in strategic decisions regarding artificial intelligence. In addition, Sonja is professor at Technical University in Munich
Bibliographic Information
Book Title: Technologies and Applications for Big Data Value
Editors: Edward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner
DOI: https://doi.org/10.1007/978-3-030-78307-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022
Hardcover ISBN: 978-3-030-78306-8Published: 29 April 2022
Softcover ISBN: 978-3-030-78309-9Published: 29 April 2022
eBook ISBN: 978-3-030-78307-5Published: 28 April 2022
Edition Number: 1
Number of Pages: XXIV, 544
Number of Illustrations: 12 b/w illustrations, 164 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Big Data, Statistics, general, Computer Applications, Knowledge based Systems