A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.

The Internet of things has produced several heterogeneous devices and data models for sensors/actuators, physical and virtual. Corresponding data must be aggregated and their models have to be put in relationships with the general knowledge to make them immediately usable by visual analytics tools, APIs, and other devices. In this paper, models and tools for data ingestion and regularization are presented to simplify and enable the automated visual representation of corresponding data. The addressed problems are related to the (i) regularization of the high heterogeneity of data that are available in the IoT devices (physical or virtual) and KPIs (key performance indicators), thus allowing such data in elements of hypercubes to be reported, and (ii) the possibility of providing final users with an index on views and data structures that can be directly exploited by graphical widgets of visual analytics tools, according to different operators. The solution analyzes the loaded data to extract and generate the IoT device model, as well as to create the instances of the device and generate eventual time series. The whole process allows data for visual analytics and dashboarding to be prepared in a few clicks. The proposed IoT device model is compliant with FIWARE NGSI and is supported by a formal definition of data characterization in terms of value type, value unit, and data type. The resulting data model has been enforced into the Snap4City dashboard wizard and tool, which is a GDPR-compliant multitenant architecture. The solution has been developed and validated by considering six different pilots in Europe for collecting big data to monitor and reason people flows and tourism with the aim of improving quality of service; it has been developed in the context of the HERIT-DATA Interreg project and on top of Snap4City infrastructure and tools. The model turned out to be capable of meeting all the requirements of HERIT-DATA, while some of the visual representation tools still need to be updated and furtherly developed to add a few features.

In the era of Industry 4.0, the New Space Economy, often called Space 4.0, has taken center stage in the satellite industry. The advent of mega‐constellations, which entails mass satellite production, necessitates state‐of‐the‐art manufacturing methods. Leveraging Industry 4.0 technologies like the Internet of Things (IoT) and Big Data analytics show great potential for improving the manufacturing, assembly, integration, and testing (MAIT) cycle. This paper focuses on how Industry 4.0 principles enhance data analysis and automation in space manufacturing, illustrated through a case study at an aerospace company, with a focus on the composite sandwich panel manufacturing line. We introduce two key contributions. First, an interactive dashboard is proposed to enhance data analytics capabilities, offering real‐time access to insights and key performance indicators (KPIs) for operators and data analysts, and enabling the exploration of customized metrics. This facilitates comprehensive analysis of the entire MAIT process, supporting trend detection, anomaly identification, and areas for improvement to facilitate data‐driven decision‐making. Second, we present two strategies to tackle the challenges posed by the constrained number of attempts to insert installations on sandwich panels. These strategies are founded on a proposed data analytics approach rooted in Markov chain principles. This approach aids operators in making informed decisions on whether to proceed with additional attempts or discard the insert. By calculating the probability of successful insertions in future attempts, our approach can suitably enhance resource usage and production timelines. The proposed approach is evaluated through stress testing, where three processes insert 212,000 sensor records into Kafka queues at varying throughputs, monitored via Metricbeat for system resource usage. Results show low CPU usage (below 20%), consistent network throughput, and stable average data insertion times after initial peaks, demonstrating the architecture's scalability and efficiency.

The demand for efficient functional size measurement (FSM) methods in the competitive software market today is undeniable. However, incomplete and imprecise system specifications pose significant challenges, particularly in scenarios that require fast, flexible, and accurate software size estimation, such as public tenders. Although the integration of conceptual models within FSMs offers a promising solution to these issues, a systematic exploration of such methods remains largely unexplored. This work evaluates FSM methods that integrate conceptual models by analyzing studies from the past 20 years. It highlights key contributions and advances in proposed conceptual model‐based FSM methods. In addition, the study examines their limitations and challenges, offering insights for future improvements. A systematic literature review (SLR) was conducted to guide the research process. The review was organized around three research questions, each targeting the study's key objectives: (1) to explore FSM methods utilizing conceptual models, (2) to summarize proposals for their improvement, and (3) to identify the limitations of the proposed enhancements. Primary studies span two decades (2004–2024), with peaks in 2008 and 2015, averaging one to two studies annually. Of the 1371 initial studies, 13 were selected using strict criteria. These studies are categorized into Measurement Techniques (30.77%), Automation (38.46%), and Application‐Specific topics (30.77%). The contributions of the primary studies are analyzed in terms of their approaches Repeatability and Validation. Repeatability is assessed by examining whether the primary studies proposed a formal model when using real datasets. In contrast, Validation focuses on whether the studies were tested in real‐world projects. A total of 46.15% of the primary studies utilize formal models, whereas 53.85% rely on nonformal models, although dataset size is often unspecified. Most studies validate their methods using 1 to 30 projects. Common Software Measurement International Consortium (COSMIC) is the most widely used FSM method (69.23%), followed by the Function Point Analysis (FPA) (15.38%) and custom Methods (15.38%), with conceptual UML models appearing in 84.61% of the studies. Key limitations, including Scalability and Generalizability, Complexity Robustness, and Flexibility, persist across all categories. Notably, Scalability and Generalizability was identified as a limitation in 75% of Measurement Techniques studies, 80% of Automation studies, and 75% of Application‐Specific studies, while Flexibility challenges were most pronounced, affecting 100% of Application‐Specific studies. The limited number of primary studies underscores a substantial research gap in conceptual model‐based FSM methods. Future research should focus on developing formal models to enhance theoretical rigor, leveraging real‐world datasets for validation, providing comprehensive methodological descriptions, and standardizing validation practices. Additionally, prioritizing advancements in FSM methods by improving scalability, generalizability, and flexibility is crucial. These enhancements will enable FSM methods to effectively manage complex systems, adapt across diverse software domains, and address application‐specific requirements, ensuring their continued relevance in dynamic and evolving software development environments.

Services for data provisioning can be extremely valuable. Organizations wrestle with deciding whether to “expose or not to expose” their data through Web APIs and, depending on the nature of their business, mull over potential benefits and drawbacks. This is because data can be exploited and misused in unanticipated ways. Rate restriction techniques are frequently used to manage data access and protect back-end computing resources. The best choice for the maximum rate at which information can be “safely” provided to clients will determine its effectiveness. An important situation involves formal service-level agreements that govern the quality of services provided by public governments and private businesses. These firms might need to decide on a rate cap for their Web APIs that prevents unauthorized clients from accurately calculating service levels while still enabling the development of valuable value-added services. In this paper, we propose a statistical model for this problem and a technique, based on sampling tools, following brute-force, binary search, and heuristic search methods, to select an appropriate rate limit, and demonstrate its validity through a case study involving a large Italian bus company.