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|a Otto, Boris.
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|a Designing Data Spaces :
|b The Ecosystem Approach to Competitive Advantage.
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|a 1st ed.
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|a Cham :
|b Springer International Publishing AG,
|c 2022.
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|c Ã2022.
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|a 1 online resource (577 pages)
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|a text
|b txt
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|a computer
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|a online resource
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 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.
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|a 15.3.5 Other Infrastructure Technologies and Trends.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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| 700 |
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|a ten Hompel, Michael.
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| 700 |
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|a Wrobel, Stefan.
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|i Print version:
|a Otto, Boris
|t Designing Data Spaces
|d Cham : Springer International Publishing AG,c2022
|z 9783030939748
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| 797 |
2 |
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|a ProQuest (Firm)
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6978268
|z Click to View
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