The concept of data spaces has emerged as a structured, scalable solution to streamline and harmonize data sharing across established ecosystems. Simultaneously, the rise of AI services enhances the extraction of predictive insights, operational efficiency, and decision-making. Despite the potential of combining these two advancements, integration remains challenging: data spaces technology is still developing, and AI services require further refinement in areas like ML workflow orchestration and energy-efficient ML algorithms. In this paper, we introduce an integrated architectural framework, developed under the Green.Dat.AI project, that unifies the strengths of data spaces and AI to enable efficient, collaborative data sharing across sectors. A practical application is illustrated through a smart farming use case, showcasing how AI services within a data space can advance sustainable agricultural innovation. Integrating data spaces with AI services thus maximizes the value of decentralized data while enhancing efficiency through a powerful combination of data and AI capabilities.

In today’s dynamic cyber threat landscape, organizations must take proactive steps to bolster their cybersecurity defenses. Cyber threat hunting is a proactive and iterative process aimed at identifying and mitigating advanced threats that may go undetected by traditional security measures. Rather than waiting for automated security systems to flag potential threats, threat hunting involves actively searching for signs of malicious activity within an organization’s network. In this paper, we present the Forensic Visualization Toolkit, a powerful tool designed for digital forensics investigations, analysis of digital evidence, and advanced visualizations to enhance cybersecurity situational awareness and risk management and empower security analysts with an intuitive and interactive tool. Through practical, real-world scenarios, we demonstrate how FVT significantly amplifies the capabilities of cybersecurity professionals, enabling them to effectively identify, analyze, and respond to threats. Furthermore, it is important to highlight that FVT has been integrated into, utilized, and continually enhanced within various EU-funded research projects over recent years.

This paper describes the performance benefits attained using enhanced network interfaces to achieve low latency communication. We make use of DMA communication mode, to send data to other nodes, while the CPU performs useful calculations. Zero-copy communication is achieved through pinned-down physical memory regions, provided by NIC's driver modules. Our testbed concerns the parallel execution of tiled nested loops onto a Linux PC cluster with PCI-SCI NICs (Dolphin D330). Tiles are essentially exchanging data and should also have large Computational grain, so that their parallel execution becomes beneficial. We schedule tiles much more efficiently by exploiting the inherent overlapping between communication and computation phases among successive, atomic tile executions. The applied nonblocking schedule resembles a pipelined data-path where computation phases are overlapped with communication ones, instead of being interleaved with them. Experimental evaluation illustrates that when using enhanced communication features such as DMA transfers, memory-mapped interfaces and zero-copy mechanisms, overall performance is considerably improved compared to using conventional, CPU and kernel bounded, communication primitives.

This paper describes the performance benefits attained using enhanced network interfaces to achieve low latency communication. We present a novel, pipelined scheduling approach which takes advantage of DMA communication mode, to send data to other nodes, while the CPUs are performing calculations. We also use zero-copy communication through pinned-down physical memory regions, provided by NIC’s driver modules. Our testbed concerns the parallel execution of tiled nested loops onto a cluster of SMP nodes with single PCI-SCI NICs inside each node. In order to schedule tiles, we apply a hyperplane-based grouping transformation to the tiled space, so as to group together independent neighboring tiles and assign them to the same SMP node. Experimental evaluation illustrates that memory mapped NICs with enhanced communication features enable the use of a more advanced pipelined (overlapping) schedule, which considerably improves performance, compared to an ordinary blocking schedule, implemented with conventional, CPU and kernel bounded, communication primitives.

This paper proposes a new method for the problem of minimizing the execution time of nested for-loops using a tiling transformation. In our approach, we are interested not only in tile size and shape according to the required communication to computation ratio, but also in overall completion time. We select a time hyperplane to execute different tiles much more efficiently by exploiting the inherent overlapping between communication and computation phases among successive, atomic tile executions. We assign tiles to processors according to the tile space boundaries thus considering the iteration space bounds. Our schedule considerably reduces overall completion time under the assumption that some part from every communication phase can be efficiently overlapped with atomic, pure tile computations. The overall schedule resembles a pipelined datapath where computations are not anymore interleaved with sends and receives to non-local processors. Experimental results in a cluster of Pentiums by using various MPI send primitives show that the total completion time is significantly reduced.