Project description
Recipe for protecting consumers from field to fork includes data
Food safety is a top priority in Europe. Data mining, aggregation and analytics are essential to address scientific, economic and societal challenges associated with food safety. The EU-funded EFRA project will develop and test solutions to discover food risk data from heterogeneous and dispersed/scarce data sources with minimal delay and appropriate format. It will also design relevant human aspects and interactions with users to measure usefulness for human risk prevention actions in real-world use-cases. By integrating big data, IoT and AI, it is possible to foster links to food data innovator communities. To achieve these goals, EFRA will design, test, and deploy tools and undertake appropriate initiatives to facilitate their uptake, elicit feedback, and engage stakeholders.
Objective
EFRA will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat. EFRAs goals are: i) develop and test solutions to discover and distil food risk data from heterogeneous and dispersed/scarce data sources with minimal delay and appropriate format; ii) design relevant human aspects & interactions with users to measure usefulness for human risk prevention actions in real-world use-cases iii) demonstrate how solutions enable the development of trustworthy, accurate, green and fair AI systems for food risk prevention iv) achieve groundbreaking advances in performance and effectiveness of food risk data discovery, collection, mining, filtering, and processing; v) integrate relevant technologies (big data, IoT, AI) to foster links to food data innovator communities vi) position its contributions into the overall ecosystem of public & private stakeholders that share data, technology and infrastructure to ensure the safety and quality of food in Europe. To achieve these goals, EFRA will design, test, and deploy tools and undertake appropriate initiatives to facilitate their uptake, elicit feedback, and engage stakeholders. The EFRA tools are: (i) EFRA Data Hub, offering intelligent crawlers and data annotation & linking modules to search, mine, process, annotate, and link dispersed, multilingual, heterogeneous, and deep/hidden food safety data sources (ii) EFRA Analytics Powerhouse: offering modules running over a green cloud HPC that distil useful insights & signals from the EFRA Data Hub to train privacy-preserving, explainable, green food risk prediction AI models (iii) EFRA Data & Analytics Marketplace: A front-facing user-friendly web app that allows interested users to discover, purchase/use, and contribute data, AI models, and analytics modules, creating an economy where data holders and data consumers engage and trade.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencesinternetinternet of things
- social scienceseconomics and businessbusiness and managementinnovation management
- natural sciencesbiological sciencesecologyecosystems
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Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
15 126 MAROUSI
Greece
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.