Extreme Food Risk Analytics

EFRA将探索极端数据挖掘、聚合和分析如何解决与欧洲消费者食用的食品的安全和质量相关的重大科学、经济和社会挑战。EFRA的目标是：i)开发和测试解决方案，以便以最小的延迟和适当的格式从异类和分散/稀缺的数据源发现和提取食品风险数据；ii)设计相关的人的方面和与用户的互动，以衡量现实世界用例中人类风险预防行动的有用性；iii)展示解决方案如何使可信赖、准确、绿色和公平的人工智能系统的开发成为可能；iv)在食品风险数据发现、收集、挖掘、过滤和处理的性能和有效性方面取得突破性进展；V)整合相关技术(大数据、物联网、人工智能)，以促进与食品数据创新者社区的联系。vi)将其贡献纳入公共和私营利益攸关方的整体生态系统，共享数据、技术和基础设施，以确保欧洲食品的安全和质量。为了实现这些目标，EFRA将设计、测试和部署工具，并采取适当的举措，以促进对它们的理解，获得反馈，并吸引利益相关者参与。EFRA工具是：(I)EFRA Data Hub，提供智能爬虫和数据注释及链接模块，以搜索、挖掘、处理、注释和链接分散、多语言、异质和深度/隐藏的食品安全数据源(Ii)EFRA Analytics Powerhouse：提供在绿色云HPC上运行的模块，这些模块从EFRA数据中心提取有用的见解和信号，以培训隐私保护、可解释的绿色食品风险预测AI模型(Iii)EFRA Data&Analytics Marketplace：面向前端的用户友好的Web应用程序，允许感兴趣的用户发现、购买/使用和贡献数据、AI模型和分析模块，创造一个数据持有者和数据消费者参与和交易的经济。

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. EFRA’s 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 AIsystemsfor 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.