Presentation demonstreate selected challanges of DATABio in area of FIWARE and IoT

1. This document is part of a project that has received funding from
the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
H2020 BIG DATA AND FIWARE AND IOT
Karel Charvat, Michal Kepka
with support of DataBio team
FIWARE Summit
ICT CHALLENGES OF THE AGRI-FOOD
VALUE CHAIN
Lesprojekt služby, University of
West Bohemia
Brussels, 31st March 2017

2. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
2The 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
3Concept 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
4Big 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
BigDataPriorityTechAreas Cross-cutting functions
Builds on

5. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
5Combining Bottom Up with Top Down principles

6. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
6WP1 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
7WP1 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
8Data Models

9. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
9Discovery view

10. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
10SensLog – 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
11SensLog – 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
12SensLog - 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
13SensLog – 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. This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. Find us at www.databio.eu
14
Thank you for your attention
Karel Charvát
LESPROJEKT sluzby
DataBio team
https://www.databio.eu/en/
https://twitter.com/DataBio_eu
https://www.linkedin.com/grou
ps/3807971
charvat@lesprojekt.cz