Big Data technologies and extreme-scale analytics a) Research and Innovation Actions (RIA)Developing new methodologies and engineering solutions addressing industrial and/or societal challenges. Proposals should cover at least one of the following technology areas (but may additionally cover others): machine learning/deep learning (especially on distributed data sets), architectures for collecting, managing and exploiting vast amounts of data; system engineering/tools to contribute to the co-design of federated/distributed systems (to involve all stakeholders/technology areas); new methods for extreme-scale analytics, deep analysis, precise predictions and automated decision-making; novel visualization techniques; data fusion and data integration technologies; standardized interconnection methods for efficient sharing of heterogeneous data pools, seamlessly using distributed tools and services.The data assets must be sufficiently large, realistic, available to the project and described in the proposal. The Commission considers that proposals requesting a contribution from the EU of between EUR 3 and 6 million would allow this area to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.b) Coordination and Support Action (CSA)To ensure coordination between the different existing and emerging activities in HPC/BD/Cloud/AI technologies, including Public-Private Partnerships, digital innovation hubs, and relevant national and regional initiatives, in particular the European Network of National Big Data Centres of Excellence[[http://www.big-data-networks.eu//]]. This action is expected to support the transition towards the activities in the Horizon Europe programme.The Commission considers that proposals requesting a contribution from the EU of EUR 1.5 million would allow this area to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts. Rapidly increasing volumes of diverse data from distributed sources create challenges for extracting valuable knowledge and commercial value from data but at the same time have huge potential towards more accurate predictions, better analytics and responsible AI. This calls for novel methods, approaches and engineering paradigms in machine learning, analytics and data management. As the success will require not only efficient data processing/management but also sufficient computing capacity and connectivity, a coordinated action with the appropriate technology areas (e.g. AI, analytics, software engineering, HPC, Cloud technologies, IoT and edge/fog/ubiquitous computing) is necessary and will contribute to a European leadership in these areas.All grants under this topic will be subject to Article 30.3 of the grant agreement (Commission right to object to transfers or licensing). a) Research and Innovation ActionsIncreased productivity and quality of system design and software development thanks to better methods, architectures and tools for complex federated/distributed systems handling extremely large volumes and streams of data;Demonstrated, significant increase of speed of data throughput and access, as measured against relevant, industry-validated benchmarks;Demonstrated adoption of results of the extreme-scale analysis and prediction in decision-making, including AI (in industry and/or society) b) Coordination and Support ActionEffective cooperation of the participating initiatives and platforms as measured by the jointly participating relevant members/users, countries/regions/cities and projects, and the organisation of common events and joint initiatives, resulting in an increased prevalence of data value chains and related technologies (HPC/BD/Cloud/IoT/AI) in the national and regional strategies.Smooth transition to Horizon Europe activities.