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.

At an increasing rate, industrial and scientific institutions need to deal with massive data flows streaming in from a multitude of sources. For instance, maritime surveillance applications combine high-velocity data streams, including vessel position signals emitted from hundreds of thousands of vessels across the world and acoustic signals of autonomous, unmanned vessels; in the financial domain, stock price forecasting and portfolio management rely on stock tick data combined with real-time information sources on various pricing indicators; at the fight against cancer, complex simulations of multi-cellular systems are used, producing extreme-scale data streams in an effort to predict the effects of drug synergies on cancer cells. In these applications, the data volumes are expected to dramatically grow in the future. Processing this data often requires not only using an HPC infrastructure, but also having data scientists, who are typically not expert programmers, program complex workflows, with a vast number of parameters to tune through time-consuming repeated programming and testing. INFORE will address these challenges and pave the way for real-time, interactive extreme-scale analytics and forecasting. The ability to forecast, as early as possible, a good approximation to the outcome of a time-consuming and resource-demanding computational task allows to quickly identify undesired outcomes and save valuable amount of time, effort and computational resources, which would otherwise be spent in vain. Consider, for example, the ability to forecast the outcome of a complex multi-cellular system simulation for tumor evolution, without the need to wait for the simulation to be completed. INFORE will also design and develop a flexible, pluggable, distributed software architecture that is programmable and set up by graphical data processing workflows. The INFORE prototype will be tested on massive real-world data from the life sciences, financial and maritime domains.

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)