Over the last few years, the European Union (EU) has placed significant emphasis on the interoperability of critical infrastructures (CIs). One of the main CI transportation infrastructures are ports. The control systems managing such infrastructures are constantly evolving and handle diverse sets of people, data, and processes. Additionally, interdependencies among different infrastructures can lead to discrepancies in data models that propagate and intensify across interconnected systems. This article introduces “BigDaM”, a Big Data Management framework for critical infrastructures. It is a cutting-edge data model that adheres to the latest technological standards and aims to consolidate APIs and services within highly complex CI infrastructures. Our approach takes a bottom-up perspective, treating each service interconnection as an autonomous entity that must align with the proposed common vocabulary and data model. By injecting strict guidelines into the service/component development’s lifecycle, we explicitly promote interoperability among the services within critical infrastructure ecosystems. This approach facilitates the exchange and reuse of data from a shared repository among developers, small and medium-sized enterprises (SMEs), and large vendors. Business challenges have also been taken into account, in order to link the generated data assets of CIs with the business world. The complete framework has been tested in the main EU ports, part of the transportation sector of CIs. Performance evaluation and the aforementioned testing is also being analyzed, highlighting the capabilities of the proposed approach.

Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests.

Large Language Models (LLMs) have recently attracted considerable attention from the scientific community, due to their advanced capabilities and potential to serve as vital tools across various industries and academic fields. An important implementation domain for LLMs is Data Science, in which they could enhance the efficiency of Data Analysis and Profiling tasks.With the utilization of LLMs in Data Analytics tools, end-users could directly issue data analysis queries in natural language, bypassing the need for specialized user interfaces. However, due to the sensitive nature of certain data in some organizations, it is unwise to consider using established, cloud-based LLMs. This article explores the feasibility and effectiveness of a standalone, offline LLM in generating code for performing data analytics, given a set of natural language queries. A methodology tailored to a code-specific LLM is presented, evaluating its performance in generating Python Spark code and successfully producing the desired result. The model is assessed on its efficiency and ability to handle natural language queries of varying complexity, exploring the potential for wider adoption of offline LLMs in future data analysis frameworks and software solutions.