Designing data spaces : : the ecosystem approach to competitive advantage / / editors, Boris Otto, Michael Ten Hompel, Stefan Wrobel.

• TeilnehmendeR: Otto, Boris.; ten Hompel, Michael.; Wrobel, Stefan.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2022.; ©2022.
• Year of Publication: 2022
• Language: English
• Physical Description: 1 online resource (xv, 580 pages) :; illustrations (chiefly color)

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I "Foundations and Contexts" provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II "Data Space Technologies" subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various "Use Cases and Data Ecosystems" from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several "Solutions and Applications" including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more.

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.