- LinkedTV (EU FP7‐CP, 2011‐2015): After 42 months of research and development completed by 12 partners across Europe, the LinkedTV project has produced services, tools and documents, enabling a new generation of TV applications. From the beginning, the project aimed at making the vision of Linked Television become reality.
- InVID – In Video Veritas. (EU H2020 Innovation Action, 2016-2018) The InVID innovation action develops a knowledge verification platform providing services to detect, authenticate and check the reliability and accuracy of newsworthy video files and video content spread via social media. InVID enable novel newsroom applications for broadcasters, news agencies, newspapers and publishers to integrate social media content into their news output without struggling to know if they can trust the material or how they can reach users to ask permission for re-use.
- ASAP – Adaptable Scalable Analytics Platform (EU 7th Framework Program 2014-2017). The project develops an open-source execution framework for scalable data analytics. It assumes that no single execution model is suitable for all types of tasks, and that no single data model is suitable for all types of data. ASAP provides resource elasticity, locality and scheduling abstraction, fault-tolerance and the ability to handle large sets of irregular distributed data.
- MeMAD (H2020, 2018-2020). The MeMAD project will develop methods for an efficient re-use and re-purpose of multilingual audiovisual content targeting to revolutionize video management and digital storytelling in broadcasting and media production.
- NoTube (FP7, 2009-2012). NoTube focused on the personalization of TV program recommendation based on EPG level metadata annotation (developed by VUA). ReTV extends this idea across all content vectors and re-using topical annotations (developed by VUA) not just for recommendation but also better understanding of the audience. NoTube was one of the first efforts to annotate TV programming semantically and use those annotations to power personalized recommendations for viewers.