Big Data in Bioeconomy : : Results from the European DataBio Project.
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| Place / Publishing House: | Cham : : Springer International Publishing AG,, 2021. {copy}2021. |
| Year of Publication: | 2021 |
| Edition: | 1st ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | 1 online resource (416 pages) |
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Table of Contents:
- Intro
- Foreword
- Introduction
- Glossary
- Contents
- Part I Technological Foundation: Big Data Technologies for BioIndustries
- 1 Big Data Technologies in DataBio
- 1.1 Basic Concepts of Big Data
- 1.2 Pipelines and the BDV Reference Model
- 1.3 Open, Closed and FAIR Data
- 1.4 The DataBio Platform
- 1.5 Introduction to the Technology Chapters
- Literature
- 2 Standards and EO Data Platforms
- 2.1 Introduction
- 2.2 Standardization Organizations and Initiatives
- 2.2.1 The Role of Location in Bioeconomy
- 2.2.2 The Role of Semantics in Bioeconomy
- 2.3 Architecture Building Blocks for Cloud Based Services
- 2.4 Principles of an Earth Observation Cloud Architecture for Bioeconomy
- 2.4.1 Paradigm Shift: From SOA to Web API
- 2.4.2 Data and Processing Platform
- 2.4.3 Exploitation Platform
- 2.5 Standards for an Earth Observation Cloud Architecture
- 2.5.1 Applications and Application Packages
- 2.5.2 Application Deployment and Execution Service (ADES)
- 2.5.3 Execution Management Service (EMS)
- 2.5.4 AP, ADES, and EMS Interaction
- 2.6 Standards for Billing and Quoting
- 2.7 Standards for Security
- 2.8 Standards for Discovery, Cataloging, and Metadata
- 2.9 Summary
- References
- Part II Data Types
- 3 Sensor Data
- 3.1 Introduction
- 3.2 Internet of Things in Bioeconomy Sectors
- 3.3 Examples from DataBio
- 3.3.1 Gaiatrons
- 3.3.2 AgroNode
- 3.3.3 SensLog and Data Connectors
- 3.3.4 Mobile/Machinery Sensors
- References
- 4 Remote Sensing
- 4.1 Introduction
- 4.2 Earth Observation Relation to Big Data
- 4.3 Data Formats, Storage and Access
- 4.3.1 Formats and Standards
- 4.3.2 Data Sources
- 4.4 Selected Technologies
- 4.4.1 Metadata Catalogue
- 4.4.2 Object Storage and Data Access
- 4.5 Usage of Earth Observation Data in DataBio's Pilots
- References
- 5 Crowdsourced Data.
- 5.1 Introduction
- 5.2 SensLog VGI Profile
- 5.3 Maps as Citizens Science Objects
- References
- 6 Genomics Data
- 6.1 Introduction
- 6.2 Genomic and Other Omics Data in DataBio
- 6.3 Genomic Data Management Systems
- References
- Part III Data Integration and Modelling
- 7 Linked Data and Metadata
- 7.1 Introduction
- 7.2 Metadata
- 7.3 Linked Data
- 7.4 Linked Data Best Practices
- 7.5 The Linked Open Data (LOD) Cloud
- 7.6 Enterprise Linked Data (LED)
- References
- 8 Linked Data Usages in DataBio
- 8.1 Introduction
- 8.2 Linked Data Pipeline Instantiations in DataBio
- 8.2.1 Linked Data in Agriculture Related to Cereals and Biomass Crops
- 8.2.2 Linked Sensor Data from Machinery Management
- 8.2.3 Linked Open EU-Datasets Related to Agriculture and Other Bio Sectors
- 8.2.4 Linked (Meta) Data of Geospatial Datasets
- 8.2.5 Linked Fishery Data
- 8.3 Experiences from DataBio with Linked Data
- 8.3.1 Usage and Exploitation of Linked Data
- 8.3.2 Experiences in the Agricultural Domain
- 8.3.3 Experiences with DBpedia
- References
- 9 Data Pipelines: Modeling and Evaluation of Models
- 9.1 Introduction
- 9.2 Modelling Data Pipelines
- 9.2.1 Modelling Software Components
- 9.2.2 Integrating Components into Data Pipelines
- 9.3 Models Quality Metrics
- 9.3.1 Metrics for the Quality of the Modelling with Modelio
- 9.3.2 ArchiMate Comprehensibility Metrics
- 9.3.3 Metrics for Model's Size
- 9.4 Conclusion and Future Vision
- References
- Part IV Analytics and Visualization
- 10 Data Analytics and Machine Learning
- 10.1 Introduction
- 10.2 Market
- 10.3 Technology
- 10.3.1 Data Analysis Process
- 10.3.2 Statistical Methods
- 10.3.3 Data mining
- 10.3.4 Machine Learning
- 10.4 Experiences in DataBio
- 10.4.1 Data Analytics in Agriculture
- 10.4.2 Data Analytics in Fishery
- References.
- 11 Real-Time Data Processing
- 11.1 Introduction and Motivation
- 11.2 Market
- 11.3 Technical Characteristics
- 11.4 Event Processing Tools
- 11.5 Experiences in DataBio
- 11.6 Conclusions
- References
- 12 Privacy-Preserving Analytics, Processing and Data Management
- 12.1 Privacy-Preserving Analytics, Processing and Data Management
- 12.2 Technology
- 12.2.1 Secure Multi-Party Computation
- 12.2.2 Trusted Execution Environments
- 12.2.3 Homomorphic Encryption
- 12.2.4 On-The-Fly MPC by Multi-Key Homomorphic Encryption
- 12.2.5 Comparison of Methods
- 12.3 Secure Machine Learning of Best Catch Locations
- 12.4 Pipeline
- 12.5 Model Development
- 12.6 User Interface
- 12.7 Conclusions and Business Impact
- References
- 13 Big Data Visualisation
- 13.1 Advanced Big Data Visualisation
- 13.2 Techniques for Visualising Very Large Amounts of Geospatial Data
- 13.2.1 Map Generalisation
- 13.2.2 Rendered Images Versus the "Real" Data
- 13.2.3 Use of Graphics Processing Units (GPUs)
- 13.3 Examples from DataBio Project
- 13.3.1 Linked Data Visualisation
- 13.3.2 Complex Integrated Data Visualisation
- 13.3.3 Web-Based Visualisation of Big Geospatial Vector Data
- 13.3.4 Visualisation of Historical Earth Observation
- 13.3.5 Dashboard for Machinery Maintenance
- References
- Part V Applications in Agriculture
- 14 Introduction of Smart Agriculture
- 14.1 Situation
- 14.2 Precision Agriculture
- 14.3 Smart Agriculture
- References
- 15 Smart Farming for Sustainable Agricultural Production
- 15.1 Introduction, Motivation and Goals
- 15.2 Pilot Set-Up
- 15.3 Technology Used
- 15.3.1 Technology Pipeline
- 15.3.2 Data Used in the Pilot
- 15.3.3 Reflection on Technology Use
- 15.4 Business Value and Impact
- 15.4.1 Business Impact of the Pilot
- 15.4.2 Business Impact of the Technology on General Level.
- 15.5 How to Guideline for Practice When and How to Use the Technology
- 15.6 Summary and Conclusions
- 16 Genomic Prediction and Selection in Support of Sorghum Value Chains
- 16.1 Introduction, Motivation and Goals
- 16.2 Pilot Set-Up
- 16.3 Technology Used
- 16.3.1 Phenomics
- 16.3.2 DNA Isolation, Next-Generation Sequencing/Genotyping, and Bioinformatics
- 16.3.3 Genomic Predictive and Selection Analytics
- 16.4 Business Value and Impact
- 16.5 How to Guideline for Practice When and How to Use the Technology
- 16.6 Summary and Conclusions
- References
- 17 Yield Prediction in Sorghum (Sorghum bicolor (L.) Moench) and Cultivated Potato (Solanum tuberosum L.)
- 17.1 Introduction, Motivation, and Goals
- 17.2 Pilot Set-Up
- 17.3 Technology Used and Yield Prediction
- 17.3.1 Reflection on the Availability and Quality of Data
- 17.4 Business Value and Impact
- 17.5 How to Guideline for Practice When and How to Use the Technology
- 17.6 Summary and Conclusions
- References
- 18 Delineation of Management Zones Using Satellite Imageries
- 18.1 Introduction, Motivation and Goals
- 18.1.1 Nitrogen Plant Nutrition Strategies in Site-Specific Crop Management
- 18.2 Pilot Set-Up
- 18.3 Technology Used
- 18.4 Exploitation of Results
- References
- 19 Farm Weather Insurance Assessment
- 19.1 Introduction, Motivation and Goals
- 19.2 Pilot Set-Up
- 19.3 Technology Used
- 19.3.1 Technology Pipeline
- 19.3.2 Reflection on Technology Use
- 19.4 Business Value and Impact
- 19.4.1 Business Impact of the Pilot
- 19.4.2 Business Impact of the Technology on General Level
- 19.5 How-to-Guideline for Practice When and How to Use the Technology
- 19.6 Summary and Conclusion
- References
- 20 Copernicus Data and CAP Subsidies Control
- 20.1 Introduction, Motivation, and Goals
- 20.2 Pilot Set-Up
- 20.3 Technology Used.
- 20.3.1 Technology Pipeline
- 20.3.2 Data Used in the Pilots
- 20.3.3 Reflections on Technology Use
- 20.4 Business Value and Impact
- 20.4.1 Business Impact of the Pilot
- 20.4.2 Business Impact of the Technology on General Level
- 20.5 How-to-Guideline for Practice When and How to Use the Technology
- 20.6 Summary and Conclusion
- References
- 21 Future Vision, Summary and Outlook
- 21.1 Summary of the Agriculture Pilots Outcomes
- 21.2 Evaluation of the Implemented Technologies and Future Vision
- 21.3 Outlook on Further Work in Smart Agriculture
- References
- Part VI Applications in Forestry
- 22 Introduction-State of the Art of Technology and Market Potential for Big Data in Forestry
- 22.1 Evolving Technologies and Growing Data Volumes
- 22.2 Expanding Market
- 22.3 DataBio Forestry Pilots
- References
- 23 Finnish Forest Data-Based Metsään.fi-services
- 23.1 Introduction
- 23.2 Background and Objectives
- 23.3 Services
- 23.4 Technology Pipeline
- 23.5 Components and Data Sets
- 23.6 Results
- 23.7 Perspective
- 23.8 Benefits and Business Impact
- 23.9 Future Vision
- 23.10 More Information
- Literature
- 24 Forest Variable Estimation and Change Monitoring Solutions Based on Remote Sensing Big Data
- 24.1 Introduction, Motivation, and Goals
- 24.2 Pilot Set-Up
- 24.3 Technology Used
- 24.3.1 Technology Pipeline
- 24.3.2 Data Used in the Pilot
- 24.3.3 Reflection on Technology Use
- 24.4 Business Value and Impact
- 24.5 How-to-Guideline for Practice When and How to Use the Technology
- 24.6 Summary and Conclusion
- References
- 25 Monitoring Forest Health: Big Data Applied to Diseases and Plagues Control
- 25.1 Introduction, Motivation, and Goals
- 25.2 Pilot Setup
- 25.3 Technology Used
- 25.3.1 Technology Pipeline
- 25.3.2 Data Used in the Pilot
- 25.3.3 Reflection on Technology Use.
- 25.4 Business Value and Impact.