New Horizons for a Data-Driven Economy : : A Roadmap for Usage and Exploitation of Big Data in Europe.
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| Place / Publishing House: | Cham : : Springer International Publishing AG,, 2016. {copy}2016. |
| Year of Publication: | 2016 |
| Edition: | 1st ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (312 pages) |
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Table of 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.