Info
Designing data spaces : the ecosystem approach to competitive advantage / / editors, Boris Otto, Michael Ten Hompel, Stefan Wrobel
| Designing data spaces : the ecosystem approach to competitive advantage / / editors, Boris Otto, Michael Ten Hompel, Stefan Wrobel |
| Autore | Otto Boris |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (xv, 580 pages) : illustrations (chiefly color) |
| Altri autori (Persone) |
OttoBoris
ten HompelMichael WrobelStefan |
| Soggetto topico |
Database management
Information technology |
| Soggetto non controllato |
Data Spaces
GAIA-X Data Lakes Big Data Information Retrieval Information Systems Applications Data Ecosystems Data Integration Data Security |
| ISBN | 3-030-93975-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996483157003316 |
Otto Boris
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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The Elements of Big Data Value [[electronic resource] ] : Foundations of the Research and Innovation Ecosystem
| The Elements of Big Data Value [[electronic resource] ] : Foundations of the Research and Innovation Ecosystem |
| Autore | Curry Edward |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (412 p.) |
| Altri autori (Persone) |
MetzgerAndreas
ZillnerSonja PazzagliaJean-Christophe García RoblesAna |
| Soggetto topico |
Information retrieval
Business & management Research & development management Information technology industries Databases |
| Soggetto non controllato |
Information Storage and Retrieval
Business and Management, general Innovation/Technology Management The Computer Industry Big Data Innovation and Technology Management Technology Commercialization Digital Transformation Innovation Spaces Data-Driven Innovation Data Analytics Technology Management Data Ecosystems Data Protection Big Data Business Models Open Access Information retrieval Data warehousing Business & Management Research & development management Industrial applications of scientific research & technological innovation Information technology industries Databases |
| ISBN | 3-030-68176-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464495403316 |
|
Curry Edward
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||
| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 |
| Autore | Curry Edward |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2021 |
| Descrizione fisica | 1 online resource (412 p.) |
| Altri autori (Persone) |
MetzgerAndreas
ZillnerSonja PazzagliaJean-Christophe García RoblesAna |
| Collana | Computer Science Series |
| Soggetto topico |
Information retrieval
Business & management Research & development management Information technology industries Databases |
| Soggetto non controllato |
Information Storage and Retrieval
Business and Management, general Innovation/Technology Management The Computer Industry Big Data Innovation and Technology Management Technology Commercialization Digital Transformation Innovation Spaces Data-Driven Innovation Data Analytics Technology Management Data Ecosystems Data Protection Big Data Business Models Open Access Information retrieval Data warehousing Business & Management Research & development management Industrial applications of scientific research & technological innovation Information technology industries Databases |
| ISBN | 3-030-68176-9 |
| Classificazione | BUS042000BUS070030BUS087000COM021000COM030000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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: Ireland´s 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/Madrid´s 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. |
| Record Nr. | UNINA-9910488709403321 |
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Curry Edward
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| Cham, : Springer International Publishing AG, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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