The ARIADNE project plans to bring together a novel high frequency advanced radio architecture and an Artificial Intelligence (AI) network processing and management approach into a new type of intelligent communications system Beyond 5G. The new intelligent system approach is necessary because the scale and complexity of the new radio attributes in the new frequency ranges cannot be optimally operated using traditional network management approaches. The ARIADNE project will enable efficient high-bandwidth wireless communications by developing three complementary but critical new technologies for Beyond 5G networks in an integrated and innovative way: ARIADNE will develop new radio technologies for communications using the above 100Ghz D-Band frequency ranges, (Pillar 1) ARIDANE will exploit the opportunities emerging for advanced connectivity based on metasurfaces where objects in the environment can become tuneable reflectors for shaping the propagation environment in D-band. (Pillar 2) ARIDANE will employ Machine Learning and Artificial Intelligence techniques to management necessary for the high-frequency communications and dynamic assignment and reconfiguration of the metasurfaces to provide continuous reliable High Bandwidth connections in the Beyond 5G scenario. (Pillar 3)

The vision of CREXDATA is to develop a generic platform for real-time critical situation management including flexible action planning and agile decision making over streaming data of extreme scale and complexity. CREXDATA develops the algorithmic apparatus, software architectures and tools for federated predictive analytics and forecasting under uncertainty. The envisioned framework boosts proactive decision making providing highly accurate and transparent short- and long-term forecasts, explainable via advanced visual analytics and accurate, real-time, augmented reality facilities. To achieve its vision, CREXDATA will develop a next generation Prediction-as-a-Service (PaaS) system where action planners will easily register their multimodal data stream sources and compute resource federations and graphically design predictive analytics workflows including (i) data ingestion, fusion, (ii) simulation, (iii) federated learning for pattern extraction and (iv) multiresolution forecasting operators. Decision makers will receive back extremely precise forecasted representations of future worlds reasoned about using transparent AI facilities and with reduced complexity via visual analytics and intuitive augmented reality provided on-site or remotely. The CREXDATA architecture incorporates 10 exploitable assets based on cutting edge research, which will significantly outperform the current state of practice in respective fields. CREXDATA will be evaluated in three use cases where real-time critical action planning and timely decision making are of utmost importance: i) maritime domain, for forecasting hazardous situations at sea and impose safer navigational routes, ii) weather emergency management, to allow authorities and first responders proactively act so as to avoid or reduce the impact and speed up recovery from natural disasters, and iii) health crisis management, to limit pandemic outbreaks and come up with non-pharmaceutical means of patient treatment.

STELAR will design, develop, evaluate, and showcase an innovative Knowledge Lake Management System (KLMS) to support and facilitate a holistic approach for FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready (high-quality, reliably labeled) data. The STELAR KLMS will allow to (semi-)automatically turn a raw data lake into a knowledge lake. This is achieved by (1) enhancing the data lake with a knowledge layer, and (2) developing and integrating a set of data management tools and workflows. The knowledge layer will comprise: (a) a data catalog offering automatically enhanced metadata for the raw data assets in the lake, and (b) a knowledge graph that semantically describes and interlinks these data assets using suitable domain ontologies and vocabularies. The provided tools and workflows will offer novel functionalities for: (a) data discovery and quality management; (b) data linking and alignment; and (c) data annotation and synthetic data generation. The KLMS will combine both human-in-the-loop and automatic approaches, to leverage background knowledge of domain experts while minimizing their involvement. To reduce manual effort and time, it will increase the automation of finding and selecting relevant data sources, configuring, and tuning the involved data management tools, and designing, executing, and monitoring end-to-end data processing workflows adapted to different user needs. The KLMS will include specialized tools and functions for geospatial, temporal, and textual data. An organization, ranging from a data-intensive SME to the operator of a data marketplace, will be able to use the STELAR KLMS to increase the readiness of its data assets for use in AI applications and for being shared and exchanged within a common data space. The STELAR KLMS will be pilot tested in diverse, real-world use cases in the agrifood data space, one of the nine data spaces of strategic societal and economic importance identified in the European Strategy for Data.