Table of Contents: Big Data in Bioeconomy :

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

Bibliographic Details
Main Author:	Södergård, Caj.
Other Authors:	Mildorf, Tomas., Habyarimana, Ephrem., Berre, Arne J., Fernandes, Jose A., Zinke-Wehlmann, Christian.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2021.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

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.

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

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