The mission of BDVe is to support the Big Data Value PPP in realizing a vibrant data-driven EU economy or said in other words, BDVe will support the implementation of the PPP to be a SUCCESS. Behind that mission, there are multiple goals to achieve, which should be taken into full consideration when defining the directions of the PPP. Some of the most challenging ones are: (1) achieving a more competitive landscape of European Big Data providers, leading to bigger market share; (2) creating the context for a more competitive EU industry (transport, manufacturing, public sector, agrifood, media, energy…) in the advent of a data-driven revolution where many traditional players will have to transform their processes and re-think their business if they want to remain completive –or in some cases, just to survive-; (3) ensuring the sustainability of the investments and actions triggered by the PPP. BDVe has broken down those high-level goals into 7 major priorities for the project: • Being accurately informed about most important facts in Big Data so that we have a solid basis to support the decision-making process in the PPP • Supporting the implementation of the Big Data PPP from an operational point of view • Developing a vibrant community around the PPP • Supporting the development of a European network of infrastructures and centers of excellence around Big Data • Setting-up a professional Communications strategy • Setting up a framework that supports the acceleration of data-driven businesses, and • Ensuring the sustainability of the investments and actions triggered by the PPP. The BDVe consortium includes a set of partners that have shown commitment and dedication to the success of the PPP for several years. They have already invested and they have committed to invest along the coming years. We believe that this CSA cannot be a neutral action that offers operational support without further commitment.

The aim of the FREME innovative action is to establish an “Open Framework of E-Services for Multilingual and Semantic Enrichment of Digital Content”. Six enrichment services will be designed, piloted, and validated during the action. Their innovation, usability, and robustness will be shaped by four real world business cases that will bring FREME data innovation and technology transfer directly to the market: (1) authoring and publishing multilingually and semantically enriched eBooks; (2) integrating semantic enrichment into multilingual content during localisation; (3) enhancing cross-language sharing and access to open data and (4) empowering personalised content recommendations. FREME innovation will lead to new business models for digital content and Big Data markets and will contribute to EU competiveness and new job profiles in the digital content management realm. FREME addresses specific challenges of topic ICT 15-2014: 1) FREME will improve the ability of EU companies to build innovative multilingual data products and services by providing six innovative, user-friendly, and robust enrichment services that are accessible both via APIs and GUIs; 2) FREME will remove technological barriers for multilingual content technologies by allowing to use data assets in an interoperable, reusable, and aggregatable way across sector, borders, and languages; 3) FREME will transfer existing mature multilingual and semantic technologies and cloud-based infrastructures previously developed by partners and available on their 7, 8, or 9 technology readiness level for action as value-adding components to content and data value chains during; 4) FREME business cases will validate the action concept, approach, and ambition within the entire content and data value chain. The core of the FREME consortium are companies who define business cases, partners who shape data innovation and technology transfer including their expertise in market validation and business modelling and planning.

The DOMINO-E project, proposed by a consortium of European organisations, including scientific institutes and SMEs and led by Airbus Defence and Space, aims at solving the key challenge of availability and reactivity of earth observations from space, by enabling multi-mission accessibility on a scalable and automated way. The implementation of a multi-mission/multi-sensor federation layer allows the end-user to address a variety of acquisition assets using scheduling and optimization algorithms. The orchestration between the users’ patrimonial missions and the third party missions is based on reactivity, persistence, precision and costs criteria, while user experience is improved thanks to cognitive assistants. The challenge is to overcome the current technological, architectural and economical roadblocks of existing mission ground segments: mono-mission architectures, un-harmonized interfaces between different ground segments, inexistent or crude multi-mission collaborative coverage and dispatch services. DOMINO-E consists of designing, analysing and modelling the multi-mission federation layer based on users’ requirements and is supported by demonstrations of added value services. A market analysis is performed to assess the commercial perspectives of multi-mission federation approach in terms of client acceptance in sharing assets, industry’s make or buy strategy and SME capability to build catalogues of multi-mission services. This innovative federation layer supports the change of space industry paradigm from instrumental push to end-client vertical needs pull and allows the EU space industry to embrace the emerging data driven space market. In such, DOMINO-E contributes to European non-dependence for the development of Earth-observation technologies and foster European competitiveness by supporting SMEs in developing multi-mission services agnostic to the end-to-end or ground segment systems integrators.

The CoRDS project addresses building the next generation of artificial intelligence (AI)-powered decision support tools to allow organizations to tackle complex decision-making problems more effectively and responsibly, such as efficiently managing scarce (natural) resources and reducing their carbon footprints. These tools unify two areas of research, namely Operations Research (OR) and Machine Learning (ML). In OR, specialized optimization methods have been developed to address complex decision problems, but these rely heavily on expert knowledge, limiting their ability to adapt to changing data. Conversely, ML excels in leveraging extensive data for predictive tasks, but struggles with combinatorial optimization. Integrating OR and ML, leading to data-driven optimization (DDO) tools, presents a promising avenue to enhance decision support by combining OR's problem-solving capabilities with ML's data utilization strengths. Furthermore, DDO tools must not only provide high-quality decisions to users in low computational time, they must also comply with government and industry standards, and therefore must be safe, transparent, traceable and non-discriminatory, i.e., follow the principles of trustworthy AI, a significant challenge for most current AI systems. The expertise needed to create and apply DDO methods to real-world problems is severely lacking. The CoRDS doctoral network addresses this critical need by developing a training program to sculpt the next generation of analytics experts combining OR and ML, who will translate their research into prototype tools to address real-life problems defined in collaboration with our industrial partners across various application sectors, including logistics, healthcare, public transportation, production, finance, publishing and machine translation. The CoRDS network further delivers a training framework for others to use and expand.

The increasing complexity of security challenges combined with the accumulation of significant amounts of digital data calls for better and more widespread use of Artificial Intelligence (AI) capabilities for law enforcement agencies (LEAs). AI can provide benefits to LEAs at all levels given the right understanding, tools, data and protection while increased awareness of criminal misuse is providing an immediate and concerning threat that must be tackled rapidly. Furthermore, a community that brings together LEAs, researchers, industry, security practitioners and other actors in the security ecosystem under a coordinated and strategic effort is essential for the realisation of these efforts into operational practices. STARLIGHT presents an inclusive and sustainable vision for increasing the awareness, capability, adoption and long-term impact of AI in Europe for LEAs. Five strategic goals underpin STARLIGHT’s approach: (1) Improve the widespread UNDERSTANDing of AI across LEAs to reinforce their investigative and cybersecurity operations and the need to uphold legal, ethical and societal values; (2) Provide opportunities to LEAs to EXPLOIT AI tools and solutions in their operational work that are trustworthy, transparent and human-centric; (3) Ensure that LEAs can PROTECT their own AI systems through privacy- and security-by-design approaches, better cybersecurity tools and knowledge; (4) Raise LEAs’ expertise and capacity to COMBAT the misuse of AI-supported crime and terrorism; and (5) BOOST AI for LEAs in Europe through high-quality datasets, an interoperable and standardised framework for long term sustainability of solutions, and the creation of an AI hub for LEAs that supports a strong AI security industry and enhances the EU’s strategic autonomy in AI. STARLIGHT will ensure European LEAs lead the way in AI innovation, autonomy and resilience, addressing the challenges of now and the future, prioritising the safety and security of Europe for all.