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A comprehensive account of Big Data Research and Innovation and the impacts realized in data ecosystems
Insights, lessons learnt and frameworks for big data including technical architectures, data-driven innovation, centres of excellence, standards, policy, skills, and data-driven business models with practical recommendations based on rigorous studies
Best practices for supporting and developing data ecosystems at local, national, and international levels
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Table of contents (17 chapters)
Ecosystem Elements of Big Data Value
Research and Innovation Elements of Big Data Value
Business, Policy, and Societal Elements of Big Data Value
Emerging Elements of Big Data Value
About this book
This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations.
The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts:
· Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders.
· Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value.
· Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society.
· Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value.
Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.
- Big Data
- Digital Transformation
- Innovation Spaces
- Data-Driven Innovation
- Data Analytics
- Technology Management
- Data Ecosystems
- Data Protection
- Big Data Business Models
- Open Access
Editors and Affiliations
Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
Paluno, Universität Duisburg-Essen, Essen, Germany
Siemens AG, Munich, Germany
SAP, Mougins, France
Big Data Value Association, Bruxelles, Belgium
Ana García Robles
About the editors
Edward Curry is a Principal Investigator at the Insight SFI Research Centre for Data Analytics at NUI Galway and leads a research unit on Open Distributed Systems. Edward has made substantial contributions to semantic technologies, incremental data management (dataspaces), event processing, software engineering, as well as distributed systems and data ecosystems. He is co-founder and elected Vice President of the Big Data Value Association, an industry-led European big data community.
Andreas Metzger is senior academic councilor at the University of Duisburg-Essen, Germany, and head of Adaptive Systems and Big Data Applications at paluno, the Ruhr Institute for Software Technology. His background and research interests are software engineering and machine learning for self-adaptive systems. He serves as steering committee vice chair of NESSI, the European Technology Platform dedicated to Software, Services and Data, and as deputy secretary general of the Big Data Value Association.
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 the 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.
Jean-Christophe Pazzaglia is Chief Support Architect Higher Education & Research at SAP France and supporting SAP’s involvement in BDVA by managing the Big Data Value ecosystem project while also leading the pilot AI4Citizen in the AI4EU project. Complementary, within SAP University Alliance, he gives lectures on SAP Technologies and Design Thinking workshops. Prior to this, he was Director of the SAP Research Center Sophia Antipolis, and the principal investigator for SAP in several European and French research projects.
Ana Garcia Robles is currently Secretary General of the Big Data Value Association (BDVA). She has a strong ICT industrial background in the Telecommunications sector, with over 10 years’ experience in the design, implementation and configuration of large-scale telecom networks and services, and, in the research and techno-economical assessment of new technologies and solutions for large-scale implementation. Ana has participated in multiple research and innovation projects in the areas of Open and Big Data, IoT, Open Platforms, Digital social innovation, and many more.
Book Title: The Elements of Big Data Value
Book Subtitle: Foundations of the Research and Innovation Ecosystem
Editors: Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles
Publisher: Springer Cham
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2021
License: CC BY
Hardcover ISBN: 978-3-030-68175-3Published: 01 July 2021
Softcover ISBN: 978-3-030-68178-4Published: 01 July 2021
eBook ISBN: 978-3-030-68176-0Published: 30 June 2021
Edition Number: 1
Number of Pages: XXIII, 401
Number of Illustrations: 2 b/w illustrations, 67 illustrations in colour