This paper presents a system designed to handle real-time streaming and the prioritization of events in dynamic, high-volume data environments. The system efficiently processes incoming events and organizes them according to customizable prioritization rules, ensuring that high-priority events are addressed promptly. A standout feature of the system is its ability to support multiple prioritization rules simultaneously, offering the flexibility to modify or create new rules on the fly without disrupting the event processing pipeline. This adaptability makes the system particularly well-suited for real-time, data-driven environments such as IoT ecosystems, smart cities, industrial processes, and renewable energy systems, where the ability to prioritize and view events through different lenses is essential for operational efficiency and responsiveness. By dynamically adjusting to new rules and outputting rule-specific timelines, the system offers a powerful solution for managing and analyzing event streams in complex, rapidly evolving contexts.

Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, all centrality metrics aim at measuring the importance of nodes (according to some definition of importance), and such importance scores are used to rank the nodes in the network, therefore the rank improvement is a strictly related topic. In a given network, the rank improvement is achieved by establishing new links, therefore the question shifts to which and how many links should be collected to get a desired rank. This problem, also known as link-building has been shown to be NP-hard, and most heuristics developed failed in obtaining good performance with acceptable computational complexity. In this article, we present LB–GDM , a novel approach that leverages Geometric Deep Learning to tackle the link-building problem. To validate our proposal, 31 real-world networks were considered; tests show that LB–GDM performs significantly better than the state-of-the-art heuristics, while having a comparable or even lower computational complexity, which allows it to scale well even to large networks.

Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient.A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost.Wind farm maintenance presents, in fact, significant logistical challenges.They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself.In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms.In particular a pickup-anddelivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs.The solution was applied to a case study in a renewable energy power plant.Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

The provision of wellness in workplaces gained interest in the last decades. A factor that contributes significantly to workers' health is their diet, expecially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees' health and diseases prevention, productivity increase, economic convenience vs eating satisfaction when using canteen services. In this paper, a multi-company canteen service dataset is presented and first significant considerations, as well as future directions, are discussed.

Internet of things can be viewed as the shifting from a network of computers to a network of things.To support M2M communication, several protocols have been developed; many of them are endorsed by client-broker model with a publish-subscribe interaction mechanism. In this paper we introduce a multi broker solution where the network of brokers is inspired by social relationships. This allow data sharing among several IoT systems, leads to a reliable and effective query forwarding algorithm and the small world effect coming from mimic humans relations guarantees fast responses and good query recall.

Business Process Management (BPM) is an accepted discipline and its importance in increasing automation inside industrial environment is today recognized by all players.The complexity of modern management process will lead to chaos without a well-designed and effective BPM.Several BPM Suites were compared and BPM approach was applied to the case study of process management in a renewable energy power plant.Results both in process reduction and simplification and flow optimization obtained in the real case are discussed to state efficacy and efficiency of the adopted approach.

Location Based Services play an important role in decision-making processes, company activities or in any control and policy system in modern computer organizations.Usually LBS applications provide location-specific information only when user requests it.However, Supply Chain Management applications require to push geolocalized information directly to the user.The most discussed and requested application is Geofencing, which allows to determine the topological relation between a moving object and a set of delimited geographical areas.This paper describes the design of an innovative solution for implementing proactive location-based services suitable for application scenarios with strong time constraints, such as realtime systems, called Proactive Fast and Low Resource Geofencing Algorithm.This work was

IoT applications has some characteristics that set it apart from other fields mainly due to the multitude of different types of sensors producing data.In monitoring applications, data processing requires real-time or soft real-time responses in order to aid systems to make important decisions but also predictive analysis to leverage the potential of IoT by data mining vast datasets.This paper presents an architecture developed to efficiently process and store data coming from an huge number of distributed IoT sensors.The back-end of SeeYourBox services is currently based on the proposed architecture that has proven to be stable and meet all the requirements.

The huge amount of textual data uploaded in virtual social networks can be effectively exploited to provide (possibly accidental) information in many scenarios. In healthcare discussion forums, users interact to educate themselves about treatments, symptoms, diseases, therapies, usually providing personal health-related information when posting questions or answers. Medical Forum data can be used for feeding Personal Health Record (PHR), the part of EHR where the person can directly manage his personal medical-related information. In this work, we propose an agent-based architecture to first extract textual data from html pages in a set of medical oriented Italian social forum. Then, text mining techniques allow to discover health related information coming from the same user in different threads and/or different forums, finally feeding the PHR with such information. The architecture is presented together with a first implementation where 10 medical discussion forums are analyzed, showing promising results when extracting medical information from a subset of 3 over 10 forums.

The number and type of digital sources storing healthcare data is increasing more and more, rising the problem of collecting actually dispersed information about a single patient.In this paper we propose an agent-based system to support integration of health-related data extracted from both structured (HIS) and semi-structured (websites and social networks) sources.Integrated data are exported in HL7 format to finally feed personal health record (PHR).

SUMMARY Currently several computer‐based scenarios leverage the concept of trust as a mean to make electronic interactions (e.g., e‐commerce transactions) as reliable as possible, allowing to cope with uncertainty and risks by recommending trusted peers. Generally, the evaluation of trustworthiness can be accomplished according to many principles, from social‐based to psychology‐based; one of the commonly adopted approaches within peer‐to‐peer networks, virtual social networks, and recommendation systems is the reputation‐based trust evaluation. Because more and more large networks (even with millions of nodes) aim at leveraging trust, approaches to its assessment have to take into account the factors as efficient distributed implementation and effective security protection against malicious attacks. In this paper, we present a distributed and secure algorithm based on TrustWebRank, a metric that takes into account both personalized trust evaluation and network dynamics issues. To test our proposal both in terms of complexity and bandwidth usage, we performed simulations on a large and real dataset built from the Epinions.com recommendation system. Results show that the proposed distributed algorithm is effective and efficient, while preserving original benefits of TrustWebRank. Copyright © 2011 John Wiley & Sons, Ltd.

This paper deals with the problem of securing data transmission in home and building networks, discussing a solution to introduce both confidentiality and authentication, based on classical security mechanism and algorithms. To validate the approach the authors applied the proposed techniques to KNX, the European and international standard for home and building automation, which doesn't provide for any security mechanisms.

The use of P2P approach in e-learning is an interesting solution in particular within lifelong learning. Generally, peer-to-peer paradigm is a successful solution to the problem of resources sharing. In this field there are two open questions. First, how to advertise learning objects on the network, so that peers knows they exist. Second, how to choose the best set of learning objects. The system presented in this paper aims at answering these questions.

The introduction of the workflow management provided companies with a flexible and robust approach to the supervision and execution of the business process. A further question that must be addressed is to help companies in the reuse of already developed workflows, thus saving investments and reducing time-to-market. This paper proposes a methodology aiming at providing reuse and modularity for production workflows based on four abstraction levels arranged into two steps: process and product

The e-learning represents the new frontier of education, significantly improving the learning process. We propose an e-learning model, providing both teachers and students with an open and modular learning environment. We then focus on courses personalization, both in terms of contents and teaching materials, according to each student's needs and capabilities, also taking teacher guidelines into account. To accomplish this, we model courses/lessons as graph nodes, where arcs represent their precedence/succession relationships. We outline a course generation/presentation engine which allows the creation of personalized learning paths (subgraph) by extracting lessons, eliminating those known to the student, and arranging them into a tree including all possible paths starting from the student's possessed knowledge towards desired knowledge.

Modern browsers allow users to search and navigate the vast amount of Web data, but the significant problem of extracting desired information from such data still remains, mainly due to the lack of an explicit structure both in Web pages and sites. We present an approach to Web structuring in which both Web pages and sites are considered. In particular, we analyze the structure and semantics, aiming at highlight (possibly hidden) structural and semantic organization, therefore building an explicit logical schema, which improves Web usability (browsing, searching) and designing.

During the last decade, the interest in distance learning tools has grown thanks to the availability of bandwidth and powerful computers. Thus, distance learning has moved from a particular environments (such as industry), to a larger community. The main challenge of last generation e-learning tools is to provide courses tailored to the different student backgrounds; this pushes research to create an adaptive environment able to just-in-time craft the best path for each student. The paper deals with this problem, also presenting a Web based prototype of an e-learning tool to provide users with all paths, moving from knowledge of a student to desired knowledge.

Current manufacturing systems have a very structured production model, especially when high complexity and precision is required, as in semiconductor device manufacturing. In addition, rapid changes in both production and market requirements may occur, hence such models should also provide great flexibility. In this case study paper we introduce the model used inside STMicroelectronics facilities to define production flow, which is the sequence of operations to be performed in order to make products. We present the main characteristics of the model, focusing in particular on its object-oriented approach, with aggregational and constitutional hierarchies used to model all entities. We also introduce two flexible inheritance mechanisms used to speed-up and improve the definition of a production flow, finally presenting in detail objects finite state machine used to model their behaviour.

Traditional servers offer little support to handle situations like faults due to the approach usually followed based on mirroring and/or duplicating functions. This approach lacks in transparency with respect to the client and causes a considerable waste of resources. Some requirements of a good service provider have been highlighted such as, for instance, reliability, capability to work in a heterogeneous environment, scalability and dynamical reconfigurability. A service provider has been developed in order to satisfy the above mentioned requirements; it is based mainly on the use of multicast addressing and the agent paradigm.

CoDesign aims to integrate the design techniques of hardware and software. In this work, we present a CoDesign methodology based on a formal approach to embedded system specification. This methodology uses the Templated T-LOTOS language to specify the system during all design phases. Templated T-LOTOS is a formal language based on CCS and CSP models. Using Templated T-LOTOS, a system can be specified by observing the temporal ordering in which the events occur from the outside. In this paper we focus on the synthesis of system specified by Templated T-LOTOS. The proposed synthesis algorithm takes advantage of peculiarities of Templates T-LOTOS. Hardware modules are translated into a register transfer-level language that manages some signals in order to drive synchronization, while the software models are translated into C according to a finite state model whose operations are controlled by a scheduler. The synthesis of the Templated T-LOTOS specification is based on the direct translation of the language operators to ensure that the implemented system is the same as the specified one.

This paper faces the problems connected to hardware software codesign partitioning phase. We propose a tool which novelties are the approach to perform the choice between hardware and software implementation. To achieve this, the tool proposed benefits from the simultaneous use of fuzzy logic and genetic algorithms, which allow the performance of single modules to be evaluated without having to actually implement them. Finally, we propose an algorithm to choose a good solution.