Designing Data Spaces : : The Ecosystem Approach to Competitive Advantage.

Designing Data Spaces : : The Ecosystem Approach to Competitive Advantage.

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Bibliographic Details
:
Otto, Boris.
TeilnehmendeR:
ten Hompel, Michael.
Wrobel, Stefan.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2022.
Ã2022.
Year of Publication:2022
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (577 pages)
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Table of Contents:
  • Intro
  • Foreword
  • Preface
  • Contents
  • Abbreviation
  • Part I: Foundations and Context
  • Chapter 1: The Evolution of Data Spaces
  • 1.1 Data Sharing in Data Ecosystems
  • 1.1.1 The Role of Data for Enterprises
  • 1.1.2 Data Sharing and Data Sovereignty
  • 1.1.3 Example Mobility Data Space
  • 1.1.4 Need for Action and Research Goal
  • 1.2 Conceptual and Technological Foundations
  • 1.2.1 Data Spaces Defined
  • 1.2.2 Roles and Responsibilities in Data Spaces
  • 1.2.3 GAIA-X and IDS
  • 1.3 Evolutionary Stages of Data Space Ecosystems
  • 1.4 Designing Data Spaces
  • 1.4.1 Ecosystem Perspective
  • 1.4.2 Federator Perspective
  • 1.5 Summary and Outlook
  • References
  • Chapter 2: How to Build, Run, and Govern Data Spaces
  • 2.1 Data Space Design Principles
  • 2.1.1 Entirely New Services for Users Based on Enhanced Transparency and Data Sovereignty
  • 2.1.2 Level Playing Field for Data Sharing and Exchange
  • 2.1.3 Need for Data Space Interoperability: The Soft Infrastructure
  • 2.1.4 Public-Private Governance: Europe Taking the Lead in Establishing the Soft Infrastructure in a Coordinated and Collabora...
  • 2.2 Building Blocks for Data Spaces
  • 2.2.1 Technical Building Blocks
  • 2.2.2 Governance Building Blocks
  • 2.3 Synthesis of Building Blocks to Data Spaces
  • 2.4 Harmonized Approach to Data Space Governance
  • 2.5 The Way Forward and Convergence: Actions to Take in the Coming Digital Decade
  • References
  • Chapter 3: International Data Spaces in a Nutshell
  • 3.1 International Data Spaces
  • 3.1.1 Goals of the International Data Spaces
  • 3.1.2 Reference Architecture Model
  • 3.1.2.1 The International Data Spaces Components
  • 3.1.2.2 The International Data Spaces Roles
  • 3.1.2.3 Usage Control
  • 3.1.3 Certification
  • 3.1.3.1 Security Profiles
  • 3.1.3.2 Participant Certification
  • 3.1.3.3 Component Certification
  • 3.1.4 Open Source.
  • References
  • Chapter 4: Role of Gaia-X in the European Data Space Ecosystem
  • 4.1 A Quick Introduction to Gaia-X
  • 4.2 The Business World with Gaia-X
  • 4.2.1 Economy of Data
  • 4.2.2 Compliance
  • 4.2.3 Measuring Success
  • 4.3 The Gaia-X Principles
  • 4.3.1 Objectives
  • 4.3.2 Policy Rules and Specifications for Infrastructure Application and Data
  • 4.3.3 Federated Services in Business Ecosystems
  • 4.4 The Gaia-X Data Spaces
  • 4.4.1 Finance and Insurance
  • 4.4.2 Energy
  • 4.4.3 Automotive
  • 4.4.4 Health
  • 4.4.5 Aeronautics
  • 4.4.6 Travel
  • 4.5 The National Hub Organization and the Launching of Additional Data Spaces
  • 4.6 Conclusion: Data Spaces-The Enabler of Digital in Business
  • References
  • Chapter 5: Legal Aspects of IDS: Data Sovereignty-What Does It Imply?
  • 5.1 Data Sovereignty: Freedom of Contract and Regulation
  • 5.1.1 No Ownership or Exclusivity Rights in Data
  • 5.1.2 Usage Control: Legally and Technically
  • 5.1.3 Database Rights
  • 5.1.4 Trade Secrets
  • 5.1.5 Competition Law
  • 5.1.6 EU Strategy on Data: The Relevance of Data Spaces
  • 5.1.7 Data Governance Act: First Comments
  • 5.1.8 Personal and Non-personal Data
  • 5.1.8.1 GDPR
  • 5.1.8.2 Free Flow of Non-Personal Data Regulation
  • 5.1.9 Cybersecurity
  • 5.1.9.1 NIS Directive
  • 5.1.9.2 Cybersecurity Act
  • 5.2 Preparing Contractual Ecosystems
  • 5.2.1 Platform Contracts
  • 5.2.1.1 Key Principles
  • 5.2.1.2 Legal TestBed: A Lead Example
  • 5.2.2 Data Licensing Agreements
  • 5.2.2.1 The Contract Matrix
  • 5.2.2.2 The IDS Sample Contracts
  • 5.3 Implementing Compliance
  • 5.3.1 GDPR
  • 5.3.1.1 Controllers, Joint Controllers, and Processors
  • 5.3.1.2 Documentation
  • 5.3.1.3 Breach Notifications
  • 5.3.1.4 Enforcement and Sanctions
  • 5.3.2 Competition Law
  • 5.4 Certifications from a Legal Perspective
  • 5.4.1 Role of Procedural Rules
  • 5.4.2 Additional Aspects.
  • Chapter 6: Tokenomics: Decentralized Incentivization in the Context of Data Spaces
  • 6.1 Tokenomics in the Context of Data Spaces
  • 6.2 Token-Based Supply Chain Management
  • 6.2.1 Supply Chain Traceability
  • 6.2.2 Distributed Ledger Technology and Tokenomics
  • 6.2.3 DLT-Based Supply Chain Traceability
  • 6.3 Tokenomics in the Context of Personal Data Markets
  • 6.3.1 Personal Data Markets
  • 6.3.2 Motivational Factors for Tokenomics Approach in Personal Data Markets
  • 6.3.3 Token Design Principles for Personal Data Markets
  • 6.3.4 Derivation of Token Archetypes for PDMs
  • 6.4 Conclusions
  • References
  • Part II: Data Space Technologies
  • Chapter 7: The IDS Information Model: A Semantic Vocabulary for Sovereign Data Exchange
  • 7.1 Introduction
  • 7.2 Evolving Trust in the IDS Toward Self-Sovereign Identity
  • 7.3 Definition of Contract Clauses: The IDS Usage Contract Language and Its Core Concepts
  • 7.3.1 The Solid Access Control Model vs. IDS Usage Contract Language
  • 7.3.2 Usage Control Dimensions
  • 7.3.3 Operators for Usage Control Rules
  • 7.4 The Policy Information Point
  • 7.5 The Participant Information Service (ParIS)
  • 7.6 Conclusion: The IDS-IM as the Bridge Between Expressions, Infrastructure, and Enforcement
  • References
  • Chapter 8: Data Usage Control
  • 8.1 Introduction
  • 8.2 Usage Control
  • 8.2.1 Access Control
  • 8.2.2 Usage Control
  • 8.2.3 Usage Control Components and Communication Flow
  • 8.2.4 Specification, Management, and Negotiation
  • 8.2.5 Related Concepts
  • 8.2.5.1 Data Leak/Loss Prevention
  • 8.2.5.2 Digital Rights Management
  • 8.2.5.3 User Managed Access
  • 8.2.5.4 Windows Information Protection
  • 8.3 Usage Control in the IDS
  • 8.3.1 Usage Control Policies
  • 8.3.1.1 Policy Classes
  • 8.3.1.2 Policy Negotiation
  • 8.3.2 Usage Control Technologies
  • 8.3.2.1 Integration Concept.
  • 8.3.2.2 MY DATA Control Technologies
  • 8.3.3 Logic-Based Usage Control (LUCON)
  • 8.3.3.1 Degree (D)
  • 8.3.3.2 Data Provenance Tracking
  • 8.4 Conclusion
  • References
  • Chapter 9: Building Trust in Data Spaces
  • 9.1 Introduction
  • 9.2 Data Sovereignty and Usage Control
  • 9.2.1 Data Provider and Data Consumer
  • 9.2.2 Protection Goals and Attacker Model
  • 9.2.3 Building Blocks
  • 9.3 Certification Process
  • 9.3.1 Multiple Eye Principle
  • 9.3.2 Component Certification
  • 9.3.3 Operational Environment Certification
  • 9.4 Connector Identities and Software Signing
  • 9.4.1 Technical Implementation of the Certification Process
  • 9.4.2 Connector Identities and Company Descriptions
  • 9.4.3 Software Signing and Manifests
  • 9.5 Connector System Security
  • 9.5.1 Trusted Computing Base
  • 9.5.2 Remote Attestation
  • 9.6 Conclusion
  • References
  • Chapter 10: Blockchain Technology and International Data Spaces
  • 10.1 Introduction
  • 10.2 Blockchain Technology
  • 10.2.1 Basic Concept
  • 10.2.2 Design Parameters
  • 10.2.3 Smart Contracts
  • 10.2.4 Opportunities of Blockchain Systems
  • 10.3 Blockchain in International Data Spaces
  • 10.4 Application Examples: Industrial Use Cases
  • 10.4.1 TrackChain
  • 10.4.2 Silke
  • 10.4.3 Sinlog
  • 10.4.4 BC for Production
  • 10.5 Conclusion
  • References
  • Chapter 11: Federated Data Integration in Data Spaces
  • 11.1 Introduction
  • 11.2 Federated Data Integration Workflows in Data Spaces
  • 11.2.1 A Simple Demonstrator Scenario
  • 11.2.2 A Data Integration Workflow Solution for Data Spaces
  • 11.3 Toward Formalisms for Virtual Data Space Integration
  • 11.3.1 Logical Foundations for Data Integration
  • 11.3.2 Data Integration Tool Extensions for Data Spaces
  • References
  • Chapter 12: Semantic Integration and Interoperability
  • 12.1 Introduction
  • 12.2 The Neglected Variety Dimension.
  • 12.2.1 From Big Data to Cognitive Data
  • 12.3 Representing Knowledge in Semantic Graphs
  • 12.3.1 Representing Data Semantically
  • 12.4 RDF a Holistic Data Representation for Schema, Data, and Metadata
  • 12.5 Establishing Interoperability by Linking and Mapping between Different Data and Knowledge Representations
  • 12.6 Exemplary Data Integration in Supply Chains with ScorVoc
  • 12.7 Conclusions
  • References
  • Chapter 13: Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning
  • 13.1 Introduction
  • 13.2 Big Data, Machine Learning, and Artificial Intelligence
  • 13.3 An Open Platform for Developing AI Applications
  • 13.4 Machine Learning at the Edge
  • 13.5 Machine Learning in Digital Ecosystems
  • 13.6 Trustworthy AI Solutions
  • 13.7 Summary
  • References
  • Chapter 14: IDS as a Foundation for Open Data Ecosystems
  • 14.1 Introduction
  • 14.2 Barriers of Open Data
  • 14.3 Related Work
  • 14.4 International Data Spaces and Open Data
  • 14.4.1 IDS as an Open Data Technology
  • 14.4.2 IDS Components in an Open Data Environment
  • 14.4.3 Benefits
  • 14.5 The Public Data Space
  • 14.5.1 The Open Data Connector
  • 14.5.2 The Open Data Broker
  • 14.5.3 Use Case: Publishing Open Government Data
  • 14.6 Discussion and Conclusion
  • References
  • Chapter 15: Defining Platform Research Infrastructure as a Service (PRIaaS) for Future Scientific Data Infrastructure
  • 15.1 Introduction
  • 15.2 European Research Area
  • 15.2.1 European Research Infrastructures and ESFRI Roadmap
  • 15.2.2 European Open Science Cloud (EOSC)
  • 15.3 Technology-Driven Science Transformation
  • 15.3.1 Science Digitalization and Industry 4.0
  • 15.3.2 Transformational Role of Artificial Intelligence
  • 15.3.3 Promises of 5G Technologies
  • 15.3.4 Adopting Platform and Ecosystems Business Model for Future SDI.
  • 15.3.5 Other Infrastructure Technologies and Trends.

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