Big Data in Bioeconomy : : Results from the European DataBio Project.

• Author: Södergård, Caj.
• TeilnehmendeR: Mildorf, Tomas.; Habyarimana, Ephrem.; Berre, Arne J.; Fernandes, Jose A.; Zinke-Wehlmann, Christian.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; {copy}2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (416 pages)

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.