New Horizons for a Data-Driven Economy : A Roadmap for Usage and Exploitation of Big Data in Europe.

By: Cavanillas, Jos�e Mar�iaContributor(s): Curry, Edward | Wahlster, WolfgangMaterial type: TextTextPublisher: Cham : Springer International Publishing AG, 2016Copyright date: {copy}2016Edition: 1st edDescription: 1 online resource (312 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9783319215693Genre/Form: Electronic books.Additional physical formats: Print version:: New Horizons for a Data-Driven EconomyLOC classification: QA75.5-76.95Online resources: Click to View
Contents:
Intro -- Foreword -- Foreword -- Preface -- Book Acknowledgements -- Project Acknowledgements -- Contents -- List of Contributors -- Part I: The Big Data Opportunity -- Chapter 1: The Big Data Value Opportunity -- 1.1 Introduction -- 1.2 Harnessing Big Data -- 1.3 A Vision for Big Data in 2020 -- 1.3.1 Transformation of Industry Sectors -- 1.4 A Big Data Innovation Ecosystem -- 1.4.1 The Dimensions of European Big Data Ecosystem -- 1.5 Summary -- References -- Chapter 2: The BIG Project -- 2.1 Introduction -- 2.2 Project Mission -- 2.3 Strategic Objectives -- 2.4 Consortium -- 2.5 Stakeholder Engagement -- 2.6 Project Structure -- 2.7 Methodology -- 2.7.1 Technology State of the Art and Sector Analysis -- 2.7.1.1 Technical Working Groups -- 2.7.1.2 Sectorial Forums -- 2.7.2 Cross-Sectorial Roadmapping -- 2.7.2.1 Consolidation -- 2.7.2.2 Mapping -- 2.7.2.3 Temporal Alignment -- 2.8 Big Data Public Private Partnership -- 2.9 Summary -- References -- Part II: The Big Data Value Chain: Enabling and Value Creating Technologies -- Chapter 3: The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches -- 3.1 Introduction -- 3.2 What Is Big Data? -- 3.3 The Big Data Value Chain -- 3.4 Ecosystems -- 3.4.1 Big Data Ecosystems -- 3.4.2 European Big Data Ecosystem -- 3.4.3 Toward a Big Data Ecosystem -- 3.5 Summary -- References -- Chapter 4: Big Data Acquisition -- 4.1 Introduction -- 4.2 Key Insights for Big Data Acquisition -- 4.3 Social and Economic Impact of Big Data Acquisition -- 4.4 Big Data Acquisition: State of the Art -- 4.4.1 Protocols -- 4.4.1.1 AMQP -- 4.4.1.2 Java Message Service -- 4.4.2 Software Tools -- 4.4.2.1 Storm -- 4.4.2.2 S4 -- 4.4.2.3 Kafka -- 4.4.2.4 Flume -- 4.4.2.5 Hadoop -- 4.5 Future Requirements and Emerging Trends for Big Data Acquisition -- 4.6 Sector Case Studies for Big Data Acquisition.
4.6.1 Health Sector -- 4.6.2 Manufacturing, Retail, and Transport -- 4.6.3 Government, Public, Non-profit -- 4.6.3.1 Tax Collection Area -- 4.6.3.2 Energy Consumption -- 4.6.4 Media and Entertainment -- 4.6.5 Finance and Insurance -- 4.7 Conclusions -- References -- Chapter 5: Big Data Analysis -- 5.1 Introduction -- 5.2 Key Insights for Big Data Analysis -- 5.3 Big Data Analysis State of the Art -- 5.3.1 Large-Scale: Reasoning, Benchmarking, and Machine Learning -- 5.3.1.1 Large-Scale Reasoning -- 5.3.1.2 Benchmarking for Large-Scale Repositories -- 5.3.1.3 Large-Scale Machine Learning -- 5.3.2 Stream Data Processing -- 5.3.2.1 RDF Data Stream Pattern Matching -- 5.3.2.2 Complex Event Processing -- 5.3.3 Use of Linked Data and Semantic Approaches to Big Data Analysis -- 5.3.3.1 Entity Summarization -- 5.3.3.2 Data Abstraction Based on Ontologies and Communication Workflow Patterns -- 5.4 Future Requirements and Emerging Trends for Big Data Analysis -- 5.4.1 Future Requirements for Big Data Analysis -- 5.4.1.1 Next Generation Big Data Technologies -- 5.4.1.2 Simplicity -- 5.4.1.3 Data -- 5.4.1.4 Languages -- 5.4.2 Emerging Paradigms for Big Data Analysis -- 5.4.2.1 Communities -- 5.4.2.2 Academic Impact -- 5.5 Sectors Case Studies for Big Data Analysis -- 5.5.1 Public Sector -- 5.5.1.1 Traffic -- 5.5.1.2 Emergency Response -- 5.5.2 Health -- 5.5.3 Retail -- 5.5.4 Logistics -- 5.5.5 Finance -- 5.6 Conclusions -- References -- Chapter 6: Big Data Curation -- 6.1 Introduction -- 6.2 Key Insights for Big Data Curation -- 6.3 Emerging Requirements for Big Data Curation -- 6.4 Social and Economic Impact of Big Data Curation -- 6.5 Big Data Curation State of the Art -- 6.5.1 Data Curation Platforms -- 6.6 Future Requirements and Emerging Trends for Big Data Curation -- 6.6.1 Future Requirements for Big Data Curation.
6.6.2 Emerging Paradigms for Big Data Curation -- 6.6.2.1 Social Incentives and Engagement Mechanisms -- 6.6.2.2 Economic Models -- 6.6.2.3 Curation at Scale -- 6.6.2.4 Human-Data Interaction -- 6.6.2.5 Trust -- 6.6.2.6 Standardization and Interoperability -- 6.6.2.7 Data Curation Models -- 6.6.2.8 Unstructured and Structured Data Integration -- 6.7 Sectors Case Studies for Big Data Curation -- 6.7.1 Health and Life Sciences -- 6.7.1.1 ChemSpider -- 6.7.1.2 Protein Data Bank -- 6.7.1.3 FoldIt -- 6.7.2 Media and Entertainment -- 6.7.2.1 Press Association -- 6.7.2.2 The New York Times -- 6.7.3 Retail -- 6.7.3.1 eBay -- 6.7.3.2 Unilever -- 6.8 Conclusions -- References -- Chapter 7: Big Data Storage -- 7.1 Introduction -- 7.2 Key Insights for Big Data Storage -- 7.3 Social and Economic Impact of Big Data Storage -- 7.4 Big Data Storage State-of-the-Art -- 7.4.1 Data Storage Technologies -- 7.4.1.1 NoSQL Databases -- 7.4.1.2 NewSQL Databases -- 7.4.1.3 Big Data Query Platforms -- 7.4.1.4 Cloud Storage -- 7.4.2 Privacy and Security -- 7.4.2.1 Security Best Practices for Non-relational Data Stores -- 7.4.2.2 Secure Data Storage and Transaction Logs -- 7.4.2.3 Cryptographically Enforced Access Control and Secure Communication -- 7.4.2.4 Security and Privacy Challenges for Granular Access Control -- 7.4.2.5 Data Provenance -- 7.4.2.6 Privacy Challenges in Big Data Storage -- 7.5 Future Requirements and Emerging Paradigms for Big Data Storage -- 7.5.1 Future Requirements for Big Data Storage -- 7.5.1.1 Standardized Query Interfaces -- 7.5.1.2 Security and Privacy -- 7.5.1.3 Semantic Data Models -- 7.5.2 Emerging Paradigms for Big Data Storage -- 7.5.2.1 Increased Use of NoSQL Databases -- 7.5.2.2 In-Memory and Column-Oriented Designs -- 7.5.2.3 Convergence with Analytics Frameworks -- 7.5.2.4 The Data Hub -- 7.6 Sector Case Studies for Big Data Storage.
7.6.1 Health Sector: Social Media-Based Medication Intelligence -- 7.6.2 Finance Sector: Centralized Data Hub -- 7.6.3 Energy: Device Level Metering -- 7.7 Conclusions -- References -- Chapter 8: Big Data Usage -- 8.1 Introduction -- 8.2 Key Insights for Big Data Usage -- 8.3 Social and Economic Impact for Big Data Usage -- 8.4 Big Data Usage State-of-the-Art -- 8.4.1 Big Data Usage Technology Stacks -- 8.4.1.1 Trade-Offs in Big Data Usage Technologies -- 8.4.2 Decision Support -- 8.4.3 Predictive Analysis -- 8.4.3.1 New Business Model -- 8.4.4 Exploration -- 8.4.5 Iterative Analysis -- 8.4.6 Visualization -- 8.4.6.1 Visual Analytics -- 8.5 Future Requirements and Emerging Trends for Big Data Usage -- 8.5.1 Future Requirements for Big Data Usage -- 8.5.1.1 Specific Requirements -- 8.5.1.2 Industry 4.0 -- 8.5.1.3 Iterative Data Streams -- 8.5.1.4 Visualization -- 8.5.2 Emerging Paradigms for Big Data Usage -- 8.5.2.1 Smart Data -- 8.5.2.2 Big Data Usage in an Integrated and Service-Based Environment -- 8.5.2.3 Service Integration -- 8.5.2.4 Complex Exploration -- 8.6 Sectors Case Studies for Big Data Usage -- 8.6.1 Healthcare: Clinical Decision Support -- 8.6.2 Public Sector: Monitoring and Supervision of Online Gambling Operators -- 8.6.3 Telco, Media, and Entertainment: Dynamic Bandwidth Increase -- 8.6.4 Manufacturing: Predictive Analysis -- 8.7 Conclusions -- References -- Part III: Usage and Exploitation of Big Data -- Chapter 9: Big Data-Driven Innovation in Industrial Sectors -- 9.1 Introduction -- 9.2 Big Data-Driven Innovation -- 9.3 Transformation in Sectors -- 9.3.1 Healthcare -- 9.3.2 Public Sector -- 9.3.3 Finance and Insurance -- 9.3.4 Energy and Transport -- 9.3.5 Media and Entertainment -- 9.3.6 Telecommunication -- 9.3.7 Retail -- 9.3.8 Manufacturing -- 9.4 Discussion and Analysis -- 9.5 Conclusion and Recommendations -- References.
Chapter 10: Big Data in the Health Sector -- 10.1 Introduction -- 10.2 Analysis of Industrial Needs in the Health Sector -- 10.3 Potential Big Data Applications for Health -- 10.4 Drivers and Constraints for Big Data in Health -- 10.4.1 Drivers -- 10.4.2 Constraints -- 10.5 Available Health Data Resources -- 10.6 Health Sector Requirements -- 10.6.1 Non-technical Requirements -- 10.6.2 Technical Requirements -- 10.7 Technology Roadmap for Big Data in the Health Sector -- 10.7.1 Semantic Data Enrichment -- 10.7.2 Data Sharing and Integration -- 10.7.3 Data Privacy and Security -- 10.7.4 Data Quality -- 10.8 Conclusion and Recommendations for Health Sector -- References -- Chapter 11: Big Data in the Public Sector -- 11.1 Introduction -- 11.1.1 Big Data for the Public Sector -- 11.1.2 Market Impact of Big Data -- 11.2 Analysis of Industrial Needs in the Public Sector -- 11.3 Potential Big Data Applications for the Public Sector -- 11.4 Drivers and Constraints for Big Data in the Public Sector -- 11.4.1 Drivers -- 11.4.2 Constraints -- 11.5 Available Public Sector Data Resources -- 11.6 Public Sector Requirements -- 11.6.1 Non-technical Requirements -- 11.6.2 Technical Requirements -- 11.7 Technology Roadmap for Big Data in the Public Sector -- 11.7.1 Pattern Discovery -- 11.7.2 Data Sharing/Data Integration -- 11.7.3 Real-Time Insights -- 11.7.4 Data Security and Privacy -- 11.7.5 Real-Time Data Transmission -- 11.7.6 Natural Language Analytics -- 11.7.7 Predictive Analytics -- 11.7.8 Modelling and Simulation -- 11.8 Conclusion and Recommendations for the Public Sector -- References -- Chapter 12: Big Data in the Finance and Insurance Sectors -- 12.1 Introduction -- 12.1.1 Market Impact of Big Data -- 12.2 Analysis of Industrial Needs in the Finance and Insurance Sectors -- 12.3 Potential Big Data Applications in Finance and Insurance.
12.4 Drivers and Constraints for Big Data in the Finance and Insurance Sectors.
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Intro -- Foreword -- Foreword -- Preface -- Book Acknowledgements -- Project Acknowledgements -- Contents -- List of Contributors -- Part I: The Big Data Opportunity -- Chapter 1: The Big Data Value Opportunity -- 1.1 Introduction -- 1.2 Harnessing Big Data -- 1.3 A Vision for Big Data in 2020 -- 1.3.1 Transformation of Industry Sectors -- 1.4 A Big Data Innovation Ecosystem -- 1.4.1 The Dimensions of European Big Data Ecosystem -- 1.5 Summary -- References -- Chapter 2: The BIG Project -- 2.1 Introduction -- 2.2 Project Mission -- 2.3 Strategic Objectives -- 2.4 Consortium -- 2.5 Stakeholder Engagement -- 2.6 Project Structure -- 2.7 Methodology -- 2.7.1 Technology State of the Art and Sector Analysis -- 2.7.1.1 Technical Working Groups -- 2.7.1.2 Sectorial Forums -- 2.7.2 Cross-Sectorial Roadmapping -- 2.7.2.1 Consolidation -- 2.7.2.2 Mapping -- 2.7.2.3 Temporal Alignment -- 2.8 Big Data Public Private Partnership -- 2.9 Summary -- References -- Part II: The Big Data Value Chain: Enabling and Value Creating Technologies -- Chapter 3: The Big Data Value Chain: Definitions, Concepts, and Theoretical Approaches -- 3.1 Introduction -- 3.2 What Is Big Data? -- 3.3 The Big Data Value Chain -- 3.4 Ecosystems -- 3.4.1 Big Data Ecosystems -- 3.4.2 European Big Data Ecosystem -- 3.4.3 Toward a Big Data Ecosystem -- 3.5 Summary -- References -- Chapter 4: Big Data Acquisition -- 4.1 Introduction -- 4.2 Key Insights for Big Data Acquisition -- 4.3 Social and Economic Impact of Big Data Acquisition -- 4.4 Big Data Acquisition: State of the Art -- 4.4.1 Protocols -- 4.4.1.1 AMQP -- 4.4.1.2 Java Message Service -- 4.4.2 Software Tools -- 4.4.2.1 Storm -- 4.4.2.2 S4 -- 4.4.2.3 Kafka -- 4.4.2.4 Flume -- 4.4.2.5 Hadoop -- 4.5 Future Requirements and Emerging Trends for Big Data Acquisition -- 4.6 Sector Case Studies for Big Data Acquisition.

4.6.1 Health Sector -- 4.6.2 Manufacturing, Retail, and Transport -- 4.6.3 Government, Public, Non-profit -- 4.6.3.1 Tax Collection Area -- 4.6.3.2 Energy Consumption -- 4.6.4 Media and Entertainment -- 4.6.5 Finance and Insurance -- 4.7 Conclusions -- References -- Chapter 5: Big Data Analysis -- 5.1 Introduction -- 5.2 Key Insights for Big Data Analysis -- 5.3 Big Data Analysis State of the Art -- 5.3.1 Large-Scale: Reasoning, Benchmarking, and Machine Learning -- 5.3.1.1 Large-Scale Reasoning -- 5.3.1.2 Benchmarking for Large-Scale Repositories -- 5.3.1.3 Large-Scale Machine Learning -- 5.3.2 Stream Data Processing -- 5.3.2.1 RDF Data Stream Pattern Matching -- 5.3.2.2 Complex Event Processing -- 5.3.3 Use of Linked Data and Semantic Approaches to Big Data Analysis -- 5.3.3.1 Entity Summarization -- 5.3.3.2 Data Abstraction Based on Ontologies and Communication Workflow Patterns -- 5.4 Future Requirements and Emerging Trends for Big Data Analysis -- 5.4.1 Future Requirements for Big Data Analysis -- 5.4.1.1 Next Generation Big Data Technologies -- 5.4.1.2 Simplicity -- 5.4.1.3 Data -- 5.4.1.4 Languages -- 5.4.2 Emerging Paradigms for Big Data Analysis -- 5.4.2.1 Communities -- 5.4.2.2 Academic Impact -- 5.5 Sectors Case Studies for Big Data Analysis -- 5.5.1 Public Sector -- 5.5.1.1 Traffic -- 5.5.1.2 Emergency Response -- 5.5.2 Health -- 5.5.3 Retail -- 5.5.4 Logistics -- 5.5.5 Finance -- 5.6 Conclusions -- References -- Chapter 6: Big Data Curation -- 6.1 Introduction -- 6.2 Key Insights for Big Data Curation -- 6.3 Emerging Requirements for Big Data Curation -- 6.4 Social and Economic Impact of Big Data Curation -- 6.5 Big Data Curation State of the Art -- 6.5.1 Data Curation Platforms -- 6.6 Future Requirements and Emerging Trends for Big Data Curation -- 6.6.1 Future Requirements for Big Data Curation.

6.6.2 Emerging Paradigms for Big Data Curation -- 6.6.2.1 Social Incentives and Engagement Mechanisms -- 6.6.2.2 Economic Models -- 6.6.2.3 Curation at Scale -- 6.6.2.4 Human-Data Interaction -- 6.6.2.5 Trust -- 6.6.2.6 Standardization and Interoperability -- 6.6.2.7 Data Curation Models -- 6.6.2.8 Unstructured and Structured Data Integration -- 6.7 Sectors Case Studies for Big Data Curation -- 6.7.1 Health and Life Sciences -- 6.7.1.1 ChemSpider -- 6.7.1.2 Protein Data Bank -- 6.7.1.3 FoldIt -- 6.7.2 Media and Entertainment -- 6.7.2.1 Press Association -- 6.7.2.2 The New York Times -- 6.7.3 Retail -- 6.7.3.1 eBay -- 6.7.3.2 Unilever -- 6.8 Conclusions -- References -- Chapter 7: Big Data Storage -- 7.1 Introduction -- 7.2 Key Insights for Big Data Storage -- 7.3 Social and Economic Impact of Big Data Storage -- 7.4 Big Data Storage State-of-the-Art -- 7.4.1 Data Storage Technologies -- 7.4.1.1 NoSQL Databases -- 7.4.1.2 NewSQL Databases -- 7.4.1.3 Big Data Query Platforms -- 7.4.1.4 Cloud Storage -- 7.4.2 Privacy and Security -- 7.4.2.1 Security Best Practices for Non-relational Data Stores -- 7.4.2.2 Secure Data Storage and Transaction Logs -- 7.4.2.3 Cryptographically Enforced Access Control and Secure Communication -- 7.4.2.4 Security and Privacy Challenges for Granular Access Control -- 7.4.2.5 Data Provenance -- 7.4.2.6 Privacy Challenges in Big Data Storage -- 7.5 Future Requirements and Emerging Paradigms for Big Data Storage -- 7.5.1 Future Requirements for Big Data Storage -- 7.5.1.1 Standardized Query Interfaces -- 7.5.1.2 Security and Privacy -- 7.5.1.3 Semantic Data Models -- 7.5.2 Emerging Paradigms for Big Data Storage -- 7.5.2.1 Increased Use of NoSQL Databases -- 7.5.2.2 In-Memory and Column-Oriented Designs -- 7.5.2.3 Convergence with Analytics Frameworks -- 7.5.2.4 The Data Hub -- 7.6 Sector Case Studies for Big Data Storage.

7.6.1 Health Sector: Social Media-Based Medication Intelligence -- 7.6.2 Finance Sector: Centralized Data Hub -- 7.6.3 Energy: Device Level Metering -- 7.7 Conclusions -- References -- Chapter 8: Big Data Usage -- 8.1 Introduction -- 8.2 Key Insights for Big Data Usage -- 8.3 Social and Economic Impact for Big Data Usage -- 8.4 Big Data Usage State-of-the-Art -- 8.4.1 Big Data Usage Technology Stacks -- 8.4.1.1 Trade-Offs in Big Data Usage Technologies -- 8.4.2 Decision Support -- 8.4.3 Predictive Analysis -- 8.4.3.1 New Business Model -- 8.4.4 Exploration -- 8.4.5 Iterative Analysis -- 8.4.6 Visualization -- 8.4.6.1 Visual Analytics -- 8.5 Future Requirements and Emerging Trends for Big Data Usage -- 8.5.1 Future Requirements for Big Data Usage -- 8.5.1.1 Specific Requirements -- 8.5.1.2 Industry 4.0 -- 8.5.1.3 Iterative Data Streams -- 8.5.1.4 Visualization -- 8.5.2 Emerging Paradigms for Big Data Usage -- 8.5.2.1 Smart Data -- 8.5.2.2 Big Data Usage in an Integrated and Service-Based Environment -- 8.5.2.3 Service Integration -- 8.5.2.4 Complex Exploration -- 8.6 Sectors Case Studies for Big Data Usage -- 8.6.1 Healthcare: Clinical Decision Support -- 8.6.2 Public Sector: Monitoring and Supervision of Online Gambling Operators -- 8.6.3 Telco, Media, and Entertainment: Dynamic Bandwidth Increase -- 8.6.4 Manufacturing: Predictive Analysis -- 8.7 Conclusions -- References -- Part III: Usage and Exploitation of Big Data -- Chapter 9: Big Data-Driven Innovation in Industrial Sectors -- 9.1 Introduction -- 9.2 Big Data-Driven Innovation -- 9.3 Transformation in Sectors -- 9.3.1 Healthcare -- 9.3.2 Public Sector -- 9.3.3 Finance and Insurance -- 9.3.4 Energy and Transport -- 9.3.5 Media and Entertainment -- 9.3.6 Telecommunication -- 9.3.7 Retail -- 9.3.8 Manufacturing -- 9.4 Discussion and Analysis -- 9.5 Conclusion and Recommendations -- References.

Chapter 10: Big Data in the Health Sector -- 10.1 Introduction -- 10.2 Analysis of Industrial Needs in the Health Sector -- 10.3 Potential Big Data Applications for Health -- 10.4 Drivers and Constraints for Big Data in Health -- 10.4.1 Drivers -- 10.4.2 Constraints -- 10.5 Available Health Data Resources -- 10.6 Health Sector Requirements -- 10.6.1 Non-technical Requirements -- 10.6.2 Technical Requirements -- 10.7 Technology Roadmap for Big Data in the Health Sector -- 10.7.1 Semantic Data Enrichment -- 10.7.2 Data Sharing and Integration -- 10.7.3 Data Privacy and Security -- 10.7.4 Data Quality -- 10.8 Conclusion and Recommendations for Health Sector -- References -- Chapter 11: Big Data in the Public Sector -- 11.1 Introduction -- 11.1.1 Big Data for the Public Sector -- 11.1.2 Market Impact of Big Data -- 11.2 Analysis of Industrial Needs in the Public Sector -- 11.3 Potential Big Data Applications for the Public Sector -- 11.4 Drivers and Constraints for Big Data in the Public Sector -- 11.4.1 Drivers -- 11.4.2 Constraints -- 11.5 Available Public Sector Data Resources -- 11.6 Public Sector Requirements -- 11.6.1 Non-technical Requirements -- 11.6.2 Technical Requirements -- 11.7 Technology Roadmap for Big Data in the Public Sector -- 11.7.1 Pattern Discovery -- 11.7.2 Data Sharing/Data Integration -- 11.7.3 Real-Time Insights -- 11.7.4 Data Security and Privacy -- 11.7.5 Real-Time Data Transmission -- 11.7.6 Natural Language Analytics -- 11.7.7 Predictive Analytics -- 11.7.8 Modelling and Simulation -- 11.8 Conclusion and Recommendations for the Public Sector -- References -- Chapter 12: Big Data in the Finance and Insurance Sectors -- 12.1 Introduction -- 12.1.1 Market Impact of Big Data -- 12.2 Analysis of Industrial Needs in the Finance and Insurance Sectors -- 12.3 Potential Big Data Applications in Finance and Insurance.

12.4 Drivers and Constraints for Big Data in the Finance and Insurance Sectors.

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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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