Ensuring robust cybersecurity in modern network environments is increasingly challenging due to the growing complexity and volume of network traffic data. Traditional detection systems often fail to identify stealthy and sophisticated attacks, such as Distributed Denial of Service (DDoS), ARP poisoning, and reconnaissance scans. Moreover, many existing methods lack transparency and produce reports that are difficult for analysts to interpret, slowing both threat comprehension and response. This paper addresses these challenges by introducing a novel methodology that integrates Knowledge Graphs, XAI techniques and Large Language Models (LLMs) to enhance network threat detection, classification, explainability, and automated reporting. The proposed approach employs Graph-BERT to encode complex communication patterns and semantic relationships into enriched knowledge graphs constructed from network logs. To ensure model transparency and interpretability, Local Interpretable Model-Agnostic Explanations (LIME) are incorporated, while structured prompts guide report generation using Generative AI. Experimental results obtained on benchmark datasets demonstrate that the methodology achieves a classification accuracy exceeding 84 %, outperforming existing detection techniques. Additionally, a comprehensive evaluation involving ablation analysis, LLM-based assessments, and expert reviews shows that incorporating structured knowledge and explainability significantly enhances the clarity, correctness, and informativeness of generated reports. These findings confirm the system’s effectiveness both as a detection mechanism and as a practical tool that helps analysts understand threats and craft informed responses.

Drones used for activities such as environmental monitoring and infrastructure inspection generate vast amounts of data, requiring dedicated infrastructure for efficient management. While the cloud is a widely adopted solution, it often faces limitations such as latency, bandwidth constraints, and scalability challenges. To this scope, this paper presents a novel framework that leverages edge-cloud continuum platforms to overcome these issues. By combining the immediacy of edge computing with the computational power of the cloud, the framework processes data close to its source for real-time responsiveness and efficiently distributes tasks across multiple infrastructure layers, from edge devices to regional data centers and centralized clouds. This hybrid approach enhances scalability, efficiency, and responsiveness, addressing the demands of modern monitoring systems. The paper also addresses the lack of standardized protocols in edge-cloud configurations, a key obstacle to seamless interoperability. The proposed framework supports developers in designing and deploying applications across the edge-cloud continuum in a platform-independent manner, optimizing deployment configurations and services to meet strict quality of service (QoS) requirements. A case study on fire monitoring validates the framework, demonstrating substantial improvements in latency and scalability for critical applications such as disaster management and environmental conservation. By enabling scalable, adaptable, and cross-platform applications, the framework provides a robust solution for the complex needs of real-time, mission-critical scenarios.

In recent years, edge-cloud solutions have gained widespread adoption for efficiently collecting and analyzing IoT-generated data across various domains like urban mobility, healthcare, and smart cities. These solutions integrate resources from edge to cloud to support real-time processing and analysis tasks, reducing latency and network congestion. Big data analysis within this paradigm involves sophisticated techniques for distributed data processing, enabling applications such as predictive maintenance and smart grid management. Nevertheless, carrying out big data analysis within the edge-cloud presents several challenges, including data privacy and security, interoperability, scalability, and energy efficiency. Addressing these challenges is imperative for providing efficient and scalable solutions for data-intensive applications like federated learning, social data analysis, smart city services, and text mining. The special issue concludes with 27 scientific papers, divided into two parts for a streamlined editorial process. This editorial, as part two, presents 12 rigorously peer-reviewed papers, complementing the 15 papers covered in the previous editorial.

Abstract Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.

More and more in recent years, IT companies have adopted edge-cloud continuum solutions to efficiently perform analysis tasks on data generated by IoT devices. As an example, in the context of urban mobility, the use of edge solutions can be extremely effective in managing tasks that require real-time analysis and low response times, such as driver assistance, collision avoidance and traffic sign recognition. On the other hand, the integration with cloud systems can be convenient for tasks that require a lot of computing resources for accessing and analyzing big data collections, such as route calculations and targeted advertising. Designing and testing such hybrid edge-cloud architectures are still open issues due to their novelty, large scale, heterogeneity, and complexity. In this paper, we analyze how the compute continuum can be exploited for efficiently managing urban mobility tasks. In particular, we focus on a case study related to taxi fleets that need to find locations where they are more likely to find new passengers. Through a simulation-based approach, we demonstrate that these solutions turn out to be effective for this class of problems, especially as the number of connected vehicles increases.

Researchers and leading IT companies are increasingly proposing hybrid cloud/edge solutions, which allow to move part of the workload from the cloud to the edge nodes, by reducing the network traffic and energy consumption, but also getting low latency responses near to real time. This paper proposes a novel hybrid cloud/edge architecture for efficiently extracting Regions-of-Interest (RoI) in a large scale urban computing environment, where a huge amount of geotagged data are generated and collected through users's mobile devices. The proposal is organized in two parts: ($i$) a modeling part that defines the hybrid cloud/edge architecture capable of managing a large number of devices; (ii) a simulation part in which different design choices are evaluated to improve the performance of RoI mining algorithms in terms of processing time, network delay, task failure and computing resource utilization. Several experiments have been carried out to evaluate the performance of the proposed architecture starting from different configurations and orchestration policies. The achieved results showed that the proposed hybrid cloud/edge architecture, with the use of two novel orchestration policies (network- and utilization-based), permits to improve the exploitation of resources, also granting low network latency and task failure rate in comparison with other standard scenarios (only-edge or only-cloud).

Summary Geotagged data gathered from social media can be used to discover places‐of‐interest (PoIs) that have attracted many visitors. Since a PoI is generally identified by geographical coordinates of a single point, it is hard to match it with people trajectories. Therefore, we define an area, called region‐of‐interest (RoI), represented by the boundaries of a PoI. The main goal of this study is to discover RoIs from PoIs using spatial data mining techniques. In this paper, we propose a new parallel method for extracting RoIs from social media datasets. It consists of two main steps: (i) automatic keyword extraction and data grouping and (ii) parallel RoI extraction. The first step extracts keywords identifying the PoIs; these keywords are used to group social media items according to the places they refer to. The second step uses a Parallel Clustering Approach (ParCA) of spatial dataset to identify RoIs. ParCA exploits a parallel execution of DBSCAN on subsets of data to generate subclusters on each processing node and then merge overlapping subclusters to form global clusters. ParCA was implemented using the MapReduce model. Experiments performed over a set of PoIs in the city of Rome using social media data show that our approach is highly scalable and reaches an accuracy of 79% in detecting RoIs. On a parallel computer with 50 cores, we obtained a speedup of 52 by processing large datasets divided into 32 splits, compared with the execution time registered when each dataset is not partitioned.

Geotagged data gathered from social media can be used to discover interesting locations visited by users called Places-of-Interest (PoIs). Since a PoI is generally identified by the geographical coordinates of a single point, it is hard to match it with user trajectories. Therefore, it is useful to define an area, called Region-of-Interest ( RoI ), to represent the boundaries of the PoI’s area. RoI mining techniques are aimed at discovering ROIs from PoIs and other data. Existing RoI mining techniques are based on three main approaches: predefined shapes, density-based clustering, and grid-based aggregation. This article proposes G-RoI , a novel RoI mining technique that exploits the indications contained in geotagged social media items to discover RoIs with a high accuracy. Experiments performed over a set of PoIs in Rome and Paris using social media geotagged data, demonstrate that G-RoI in most cases achieves better results than existing techniques. In particular, the mean F 1 score is 0.34 higher than that obtained with the well-known DBSCAN algorithm in Rome RoIs and 0.23 higher in Paris RoIs.

Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15min late) and they are estimated to have an annual cost of billions of dollars. This scenario makes the prediction of flight delays a primary issue for airlines and travelers. The main goal of this work is to implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled departure and arrival time) and weather conditions at origin airport and destination airport according to the flight timetable. Airline flight and weather observation datasets have been analyzed and mined using parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15min, we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of 60min, the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks as MapReduce applications on the Cloud.

The new DVB-S2 standard, introduced by ETSI, is mainly based on three key concepts: best transmission performance, total flexibility and reasonable receiver complexity. The Adaptive Coding and Modulation (ACM) scheme is the tecnique allowing to achieve these main goals. In particular, this tecnique allows to adapt the modulation and code levels, performed at the physical layer, at channel variations in a dynamic way. However, often the others modules, composing a satellite terrestrial terminal, does not take advantage of the ACM potentiality. For this purpose, in this paper we propose an efficient packet scheduling strategy able to operate with the different MODCOD schemes of ACM. In particular, considering ACM policy, Frame efficiency and QoS requirements the scheduling strategy has the main goal of maximizing the overall performances of the system.