The Elements of Big Data Value : : Foundations of the Research and Innovation Ecosystem.

The Elements of Big Data Value : : Foundations of the Research and Innovation Ecosystem.

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Bibliographic Details
:
Curry, Edward.
TeilnehmendeR:
Metzger, Andreas.
Zillner, Sonja.
Pazzaglia, Jean-Christophe.
García Robles, Ana.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2021.
©2021.
Year of Publication:2021
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (412 pages)
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Table of Contents:
  • Intro
  • Foreword
  • Foreword
  • Foreword
  • Preface
  • Acknowledgements
  • Contents
  • Editors and Contributors
  • Part I: Ecosystem Elements of Big Data Value
  • The European Big Data Value Ecosystem
  • 1 Introduction
  • 2 What Is Big Data Value?
  • 3 Strategic Importance of Big Data Value
  • 4 Developing a European Big Data Value Ecosystem
  • 4.1 Challenges
  • 4.2 A Call for Action
  • 4.3 The Big Data Value PPP (BDV PPP)
  • 4.4 Big Data Value Association
  • 5 The Elements of Big Data Value
  • 5.1 Ecosystem Elements of Big Data Value
  • 5.2 Research and Innovation Elements of Big Data Value
  • 5.3 Business, Policy and Societal Elements of Big Data Value
  • 5.4 Emerging Elements of Big Data Value
  • 6 Summary
  • References
  • Stakeholder Analysis of Data Ecosystems
  • 1 Introduction
  • 2 Stakeholder Analysis
  • 3 Who Is a Stakeholder?
  • 4 Methodology
  • 4.1 Phase 1: Case Studies
  • 4.2 Phase 2: Cross-Case Analysis
  • 5 Sectoral Case Studies
  • 6 Cross-Case Analysis
  • 6.1 Technology Adoption Stage
  • 6.2 Data Value Chain
  • 6.3 Strategic Impact of IT
  • 6.4 Stakeholder Characteristics
  • 6.5 Stakeholder Influence
  • 7 Summary
  • References
  • A Roadmap to Drive Adoption of Data Ecosystems
  • 1 Introduction
  • 2 Challenges for the Adoption of Big Data Value
  • 3 Big Data Value Public-Private Partnership
  • 3.1 The Big Data Value Ecosystem
  • 4 Five Mechanism to Drive Adoption
  • 4.1 European Innovation Spaces (i-Spaces)
  • 4.2 Lighthouse Projects
  • 4.3 Technical Projects
  • 4.4 Platforms for Data Sharing
  • 4.4.1 Industrial Data Platforms (IDP)
  • 4.4.2 Personal Data Platforms (PDP)
  • 4.5 Cooperation and Coordination Projects
  • 5 Roadmap for Adoption of Big Data Value
  • 6 European Data Value Ecosystem Development
  • 7 Summary
  • References
  • Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far.
  • 1 Introduction
  • 2 The Big Data Value PPP
  • 2.1 BDV PPP Vision and Objectives for European Big Data Value
  • 2.2 Big Data Value Association (BDVA)
  • 2.3 BDV PPP Objectives
  • 2.4 BDV PPP Governance
  • 2.5 BDV PPP Monitoring Framework
  • 3 Main Activities and Achievements During 2018
  • 3.1 Mobilisation of Stakeholders, Outreach, Success Stories
  • 4 Monitored Achievements and Impact of the PPP
  • 4.1 Achievement of the Goals of the PPP
  • 4.2 Progress Achieved on KPIs
  • 4.2.1 Private Investments
  • 4.2.2 Job Creation, New Skills and Job Profiles
  • 4.2.3 Impact of the BDV PPP on SMEs
  • 4.2.4 Innovations Emerging from Projects
  • 4.2.5 Supporting Major Sectors and Major Domains with Big Data Technologies and Applications
  • 4.2.6 Experimentation
  • 4.2.7 SRIA Implementation and Update
  • 4.2.8 Technical Projects
  • 4.2.9 Macro-economic KPIs
  • 4.2.10 Contributions to Environmental Challenges
  • 4.2.11 Standardisation Activities with European Standardisation Bodies
  • 5 Summary and Outlook
  • References
  • Part II: Research and Innovation Elements of Big Data Value
  • Technical Research Priorities for Big Data
  • 1 Introduction
  • 2 Methodology
  • 2.1 Technology State of the Art and Sector Analysis
  • 2.2 Subject Matter Expert Interviews
  • 2.3 Stakeholder Workshops
  • 2.4 Requirement Consolidation
  • 2.5 Community Survey
  • 3 Research Priorities for Big Data Value
  • 3.1 Priority `Data Management ́-- 3.1.1 Challenges
  • 3.1.2 Outcomes
  • 3.2 Priority `Data Processing Architectures ́-- 3.2.1 Challenges
  • 3.2.2 Outcomes
  • 3.3 Priority `Data Analytics ́-- 3.3.1 Challenges
  • 3.3.2 Outcomes
  • 3.4 Priority `Data Visualisation and User Interaction ́-- 3.4.1 Challenges
  • 3.4.2 Outcomes
  • 3.5 Priority `Data Protection ́-- 3.5.1 Challenges
  • 3.5.2 Outcomes
  • 4 Big Data Standardisation
  • 5 Engineering and DevOps for Big Data
  • 5.1 Challenges.
  • 5.2 Outcomes
  • 6 Illustrative Scenario in Healthcare
  • 7 Summary
  • References
  • A Reference Model for Big Data Technologies
  • 1 Introduction
  • 2 Reference Model
  • 2.1 Horizontal Concerns
  • 2.1.1 Data Visualisation and User Interaction
  • 2.1.2 Data Analytics
  • 2.1.3 Data Processing Architectures
  • 2.1.4 Data Protection
  • 2.1.5 Data Management
  • 2.1.6 Cloud and High-Performance Computing (HPC)
  • 2.1.7 IoT, CPS, Edge and Fog Computing
  • 2.2 Vertical Concerns
  • 2.2.1 Big Data Types and Semantics
  • 2.2.2 Standards
  • 2.2.3 Communication and Connectivity
  • 2.2.4 Cybersecurity
  • 2.2.5 Engineering and DevOps for Building Big Data Value Systems
  • 2.2.6 Marketplaces, Industrial Data Platforms and Personal Data Platforms (IDPs/PDPs), Ecosystems for Data Sharing and Innovat...
  • 3 Transforming Transport Case Study
  • 3.1 Data Analytics
  • 3.2 Data Visualisation
  • 3.3 Data Management
  • 3.4 Assessing the Impact of Big Data Technologies
  • 3.5 Use Case Conclusion
  • 4 Summary
  • References
  • Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving T...
  • 1 Introduction
  • 1.1 Aim of the Chapter
  • 1.2 Context
  • 2 Challenges to Security and Privacy in Big Data
  • 3 Current Trends and Solutions in Privacy-Preserving Technologies
  • 3.1 Trend 1: User-Centred Data Protection
  • 3.2 Trend 2: Automated Compliance and Tools for Transparency
  • 3.3 Trend 3: Learning with Big Data in a Privacy-Friendly and Confidential Way
  • 3.4 Future Direction for Policy and Technology Development: Implementing the Old and Developing the New
  • 4 Recommendations for Privacy-Preserving Technologies
  • References
  • A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence
  • 1 Introduction
  • 2 Innovation Ecosystems and Centres of Excellence.
  • 2.1 What Are Centres of Excellence?
  • 3 Methodology
  • 4 Best Practice Framework for Big Data and Artificial Intelligence Centre of Excellence
  • 4.1 Environment
  • 4.1.1 Industry
  • 4.1.2 Policy
  • 4.1.3 Societal
  • 4.2 Strategic Capabilities
  • 4.2.1 Strategy
  • 4.2.2 Governance
  • 4.2.3 Structure
  • 4.2.4 Funding
  • 4.2.5 People
  • 4.2.6 Culture
  • 4.3 Operational Capabilities
  • 4.4 Impact
  • 4.4.1 Economic Impact
  • 4.4.2 Scientific Impact
  • 4.4.3 Societal Impact
  • 4.4.4 Impact Measured Through KPIs
  • 5 How to Use the Framework
  • 5.1 Framework in Action
  • 6 Critical Success Factors for Centres of Excellence
  • 6.1 Challenges
  • 6.2 Success Factors
  • 6.3 Mechanisms to Address Challenges
  • 6.4 Ideal Situation
  • 7 Summary
  • References
  • Data Innovation Spaces
  • 1 Introduction
  • 2 Introduction to the European Data Innovation Spaces
  • 3 Key Elements of an i-Space
  • 4 Role of an i-Space and its Alignment with Other Initiatives
  • 5 BDVA i-Spaces Certification Process
  • 6 Impact of i-Spaces in Their Local Innovation Ecosystems
  • 7 Cross-Border Collaboration: Towards a European Federation of i-Spaces
  • 8 Success Stories
  • 8.1 CeADAR: Irelandś Centre for Applied Artificial Intelligence
  • 8.2 CINECA
  • 8.3 EGI
  • 8.4 EURECAT/Big Data CoE Barcelona
  • 8.5 ITAINNOVA/Aragon DIH
  • 8.6 ITI/Data Cycle Hub
  • 8.7 Know-Center
  • 8.8 NCSR Demokritos/Attica Hub for the Economy of Data and Devices (ahedd)
  • 8.9 RISE/ICE by RISE
  • 8.10 Smart Data Innovation Lab (SDIL)
  • 8.11 TeraLab
  • 8.12 Universidad Politécnica de Madrid/Madridś i-Space for Sustainability/AIR4S DIH
  • 9 Summary
  • Reference
  • Part III: Business, Policy, and Societal Elements of Big Data Value
  • Big Data Value Creation by Example
  • 1 Introduction
  • 2 How Can Big Data Transform Everyday Mobility and Logistics?.
  • 3 Digitalizing Forestry by Harnessing the Power of Big Data
  • 4 GATE: First Big Data Centre of Excellence in Bulgaria
  • 5 Beyond Privacy: Ethical and Societal Implications of Data Science
  • 6 A Three-Year Journey to Insights and Investment
  • 7 Scaling Up Data-Centric Start-Ups
  • 8 Campaign Booster
  • 9 AI Technology Meets Animal Welfare to Sustainably Feed the World
  • 10 Creating the Next Generation of Smart Manufacturing with Federated Learning
  • 11 Towards Open and Agile Big Data Analytics in Financial Sector
  • 12 Electric Vehicles for Humans
  • 13 Enabling 5G in Europe
  • 14 Summary
  • References
  • Business Models and Ecosystem for Big Data
  • 1 Introduction
  • 2 Big Data Business Approaches
  • 2.1 Optimisation and Improvements
  • 2.2 Upgrading and Revaluation
  • 2.3 Monetising
  • 2.4 Breakthrough
  • 3 Data-Driven Business Opportunities
  • 4 Leveraging the Data Ecosystems
  • 4.1 Data-Sharing Ecosystem
  • 4.2 Data Innovation Ecosystems
  • 4.3 Value Networks in a Business Ecosystem
  • 5 Data-Driven Innovation Framework and Success Stories
  • 5.1 The Data-Driven Innovation Framework
  • 5.2 Examples of Success Stories
  • 5.2.1 Selectionnist
  • 5.2.2 Arable
  • 6 Conclusion
  • References
  • Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework
  • 1 Introduction
  • 2 Data-Driven Innovation
  • 2.1 What Are Business Opportunities?
  • 2.2 Characteristics of Data-Driven Innovation
  • 2.3 How to Screen Data-Driven Innovation?
  • 3 The ``Making-of ́́the DDI Framework
  • 3.1 State-of-the-Art Analysis
  • 3.2 DDI Ontology Building
  • 3.3 Data Collection and Coding
  • 3.3.1 Selection Criteria
  • 3.3.2 Sample Data Generation
  • 3.3.3 Coding of Data
  • 3.4 Data Analysis
  • 4 Findings of the Empirical DDI Research Study
  • 4.1 General Findings
  • 4.2 Value Proposition
  • 4.3 Data
  • 4.4 Technology.
  • 4.5 Network Strategies.

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