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H2020 big data and fiware an d iot

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

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H2020 big data and fiware an d iot

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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