Two major trends significantly changed the global Artificial Intelligence (AI) and Data landscape. Recent AI and Machine Learning developments are driving a paradigm shift to creating large task-agnostic foundation models pre-trained using web-scale data. Foundation models are then adapted to different downstream tasks via techniques such as fine-tuning. At the same time, we see a movement to the creation of large-scale data-sharing infrastructures. Data Spaces are an emerging approach to data management and sharing at the core of the European Data Strategy to provide access to high-quality data for AI. This paper brings together work on foundation models and data spaces into a holistic vision for Foundation Data Space Models. The paper highlights the data management requirements challenges for data spaces and details a high-level approach for foundation data space models together with a unified lifecycle for data spaces and foundation models. Finally, it sets out a research agenda.

Amidst prevailing healthcare challenges, a dynamic solution emerges, fusing knowledge graph technology, clinical trials optimization, dataspace integration, and AI innovation. This unified approach tackles issues like limited patient insights, suboptimal trial designs, and imprecise treatments. By interlinking diverse data through knowledge graphs, this method illuminates disease trends, therapeutic efficacies, and patient prognoses. AI techniques, especially machine learning, contribute predictive power by unveiling hidden patterns for accurate diagnostics, prognostics, and personalized treatments. This multidisciplinary fusion transforms clinical trials, enhancing comprehensiveness and precision through real-world data analysis and subgroup identification. In reshaping healthcare, this proposition aims to accelerate treatment personalization, elevate therapeutic efficacy, and empower informed medical decisions, encompassing the essence of ’Advancing Healthcare through Innovation: Knowledge Graphs, Clinical Trials, Dataspace, and AI’.

Foundation Models are pivotal in advancing artificial intelligence, driving notable progress across diverse areas. When merged with dataspace, these models enhance our capability to develop algorithms that are powerful, predictive, and honor data sovereignty and quality. This paper highlights the potential benefits of a comprehensive repository of Foundation Models, contextualized within dataspace. Such an archive can streamline research, development, and education by offering a comparative analysis of various models and their applications. While serving as a consistent reference point for model assessment and fostering collaborative learning, the repository does face challenges like unbiased evaluations, data privacy, and comprehensive information delivery. The paper also notes the importance of the repository being globally applicable, ethically constructed, and user-friendly. We delve into the nuances of integrating Foundation Models within dataspace, balancing the repository’s strengths against its limitations.

The security and well-being of a nation’s citizens, as well as the protection of their lives and properties, are fundamental for prosperity. Unfortunately, in recent years, we have witnessed a surge in various types of crimes such as murder, robbery, terrorism, and kidnapping. This has placed significant pressure on Law Enforcement Agencies (LEAs) to effectively prevent and detect crimes, driving them to adopt various technologies in the criminal investigation process. Among these technologies, surveillance systems have emerged as a valuable tool for monitoring human behaviors and activities, particularly in public and densely populated areas of large cities. However, video analysis in crime investigation poses significant challenges for LEAs, requiring accurate and timely detection, recognition, and tracking of objects and individuals. Addressing this issue, we present CRIMEO, a smart system designed for mining and extracting behavioral patterns from video content. CRIMEO operates in real-time and leverages an ontology-based approach to represent complex semantic events and employ video analytics. This enables LEAs to automatically detect and identify different types of crimes. CRIMEO encompasses four key phases: data collection, analysis, storage, and visualization. In the data collection phase, video data from surveillance systems is gathered and transmitted, via Apache Kafka, to the subsequent phase after the video is split into frames. The data analysis phase applies a range of video analytics techniques, including face detection and recognition, object detection, action recognition, and more, to extract behavioral patterns from the video content. These extracted patterns are then stored in the Neo4j graph database during the data storage phase. Finally, in the visualization phase, inference rules defined by LEA experts are applied to detect and visualize criminal activities. To demonstrate the effectiveness of CRIMEO, we implemented the system across multiple scenarios, showcasing its relevance in aiding LEAs in the detection of various crime types. By utilizing CRIMEO, LEAs can benefit from advanced video analysis capabilities and real-time crime detection, ultimately enhancing their ability to maintain safety and security within their jurisdictions.