1. H2020 Big Data and FIWARE anD IoT
Karel Charvat, Michal Kepka with support of DataBio team
Lesprojekt služby, University of West Bohemia
FIWARE Summit
ICT CHALLENGES OF THE AGRI-FOOD VALUE CHAIN
Brussels, 31st March 2017

2. The project in a nutshell
The industrial domain addressed
Bioeconomy
Production of best possible raw materials from agriculture, forestry and fishery for the Bioeconomy industry to produce food, energy and biomaterials
The current landscape
Few large ICT vendors so far
The opportunity
Bioeconomy can get a boost from Big Data.
Farm machines, fishing vessels, forestry machinery and remote and proximal sensors collect large quantities data.
Large scale data collection and collation enhances knowledge to increase performance and productivity in a sustainable way.
DataBio’s vision for influencing the domain
Showcase the benefits of Big Data technologies in the raw material production for the bioeconomy industry
Increase participation of European ICT industry
Project data Total budget= 16,2 M€ 48 partners, 10 of which are BDVA members 71 Associate partners Duration: 01/01/2017 – 31/12/2019

3. Concept and methodology
Variety (managing integration of all the heterogeneous data from the past - using Linked (Open) Data and semantics/ontologies etc. - and data access, queries, reporting etc. for data preparation).
Descriptive analytics and classical query/reporting (performance data, transactional data, attitudinal data, descriptive data, behavioral data, location-related data, interactional data, from many different sources)
Velocity (managing real time/sensor data from the present - complex event processing, Apache Kafka/Storm etc.)
Monitoring and real-time analytics - pilot services (in need of Velocity processing - and handling of real-time data from the present) - trigging alarms, actuators etc.
Volume (mining all the data with respect to prediction and forecasting for the future - using various types of machine learning and inductive statistical methods).
Forecasting, Prediction and Recommendation analytics - pilot services (in need of Volume processing - and processing of large amounts of data combining knowledge from the past and present, and from models, to provide insight for the future).

4. Big Data Reference Model
Data Protection
Engineering & DevOps
Standards
Data Processing Architectures
Batch, Interactive, Streaming/Real-time
Data Visualisation and User Interaction
1D, 2D, 3D, 4D, VR/AR
Data Analytics
Descriptive, Diagnostic, Predictive, Prescriptive
Data Management
Collection, Preparation, Curation, Linking, Access
(Existing) Infrastructure
Cloud, Communication (5G), HPC, IoT/CPS
Big Data Priority Tech Areas
Cross-cutting functions
Builds on

5. Combining Bottom Up with Top Down principles

6. WP1 Agriculture
Detail the pilots to be implemented on top of the provided common infrastructure;
Provide the integrated for plots, giving access to all the tools developed and to the required execution resources (in terms of data and computation);
Implement the detailed pilots according to the designs, using the e-Infrastructure services;
The Big technologies will be tested in three areas arable farming, horticulture and Subsidies an insurance, where every area will be tested in in sub-pilots with different topics and running in different countries.

7. Precision Horticulture including vine and olives
WP1 Agriculture
Precision Horticulture including vine and olives
Precision agriculture in olives, fruits, grapes and vegetables
Big Data management in greenhouse eco-systems
Arable Precision Farming
Cereals and biomass crops
Machinery management
Subsidies and insurance
Insurance
CAP reform

8. Data Models

9. Discovery view

10. SensLog – Proton CO-OPERATION
SensLog – web-based sensor data management system
CEP Proton – platform to support the development, deployment, and maintenance of event-driven applications
SensLog – own data model derived from ISO Observations&Measurements, sensor-centric
CEP Proton – data model related to IoT architecture, entity-centric

11. SensLog – Proton cooperation
Main idea:
bring CEP functionality to DataBio applications,
harmonization between observation-/sensor-centric data models and IoT architecture
SensLog – provides receiving and publishing of observations from/to web applications
Proton – provides analytical functionality to detect complex events
Communication by REST API with JSON encoding on both sides
Implmenting of NGSI-9/10 v2 on SensLog side

12. Modular solution for sensor data management on the Web
SensLog - scalability
Modular solution for sensor data management on the Web
Cooperation with tracking of agricultural machinery for hundreds of machines
Need to store set of observations every 2 seconds for each machinery

13. add rapid database for receiving data – e.g. no-SQL
SensLog – scalability
Ideas:
add rapid database for receiving data – e.g. no-SQL
paralelize receiver module that is storing to the current RDBMS
paralelize whole SensLog with RDBMS – large partitioning
Candidate tool to use – Docker – duplicates only defined components

14. Thank you for your attention Karel Charvát LESPROJEKT sluzby DataBio team ps/