Supporting consumers in making autonomous food choices that are sustainable and nutritionally complete is an increasingly complex task, that must take into account several needs to foster eating habits of health-conscious consumers, while reducing food waste and environmental impact. While generative AI and Large Language Models (LLMs) show promising results in this domain due to their natural language processing capabilities, they suffer from critical limitations, including hallucinations, knowledge gaps, and a lack of handling factual information. To mitigate such limitations, Retrieval Augmented Generation (RAG), which retrieves relevant information from external sources to enhance the capabilities of LLMs, has shown effectiveness in many domains. However, existing RAG approaches typically operate on unstructured text that lacks sophisticated symbolic representations of complex domain knowledge. This work proposes an ontology-enhanced conversational food advisory system, that integrates a modular ontology, named FoCOSA (Food Consumer-Oriented Sustainability-Aware), within several key tasks of a RAG-based system, enhancing LLM reasoning with domain knowledge, while the LLM enhances the interpretation of user requests, thus improving retrieval effectiveness and interaction fluidity. Experimental evaluations demonstrate the efficacy of the approach, and the study concludes with guidelines for selecting appropriate settings for food recommendation scenarios considering the complexity of natural language queries and other contextual factors.

Abstract The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users’ profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.

Abstract In recent years, Cyber Physical Production Systems and Digital Threads opened the vision on the importance of data modelling and management to lead the smart factory towards a full-fledged vertical and horizontal integration. Vertical integration refers to the full connection of smart factory levels from the work centers on the shop floor up to the business layer. Horizontal integration is realised when a single smart factory participates in multiple interleaved supply chains with different roles (e.g., main producer, supplier), sharing data and services and forming a Cyber Physical Production Network. In such an interconnected world, data and services become fundamental elements in the cyberspace to implement advanced data-driven applications such as production scheduling, energy consumption optimisation, anomaly detection, predictive maintenance, change management in Product Lifecycle Management, process monitoring and so forth. In this paper, we propose a methodology that guides the design of a portfolio of data-oriented services in a Cyber Physical Production Network. The methodology starts from the goals of the actors in the network, as well as their requirements on data and functions. Therefore, a data model is designed to represent the information shared across actors according to three interleaved perspectives, namely, product, process and industrial assets. Finally, multi-perspective data-oriented services for collecting, monitoring, dispatching and displaying data are built on top of the data model, according to the three perspectives. The methodology also includes a set of access policies for the actors in order to enable controlled access to data and services. The methodology is tested on a real case study for the production of valves in deep and ultra-deep water applications. Experimental validation in the real case study demonstrates the benefits of providing a methodological support for the design of multi-perspective data-oriented services in Cyber Physical Production Networks, both in terms of usability of the data navigation through the services and in terms of service performances in presence of Big Data.

Abstract Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

Digital transformation and the adoption of ICT technologies in the factory of the future are growing faster and faster. In particular, data exploration methods and techniques are enabling the development of data-intensive Remote Monitoring Services for anomaly detection and predictive maintenance purposes. Remote Monitoring Services involve different actors across organizations. The Original Equipment Manufacturer explores high volume of data collected by sensors on the monitored machines to provide anomaly detection and predictive maintenance services. Insurance agencies may provide support to sustain maintenance costs. Spare parts suppliers can schedule the delivery of mechanical parts required for maintenance interventions. In this scenario, trust among participants becomes a critical issue. On the one hand, providers of anomaly detection and predictive maintenance services as well as insurance agencies must trust the way machines have been used by collecting and analysing sensors data. On the other hand, owners of monitored machines must trust the use of collected data to implement services, based on which maintenance costs are calculated. The goal of this paper is to leverage blockchain and smart contracts to ensure the required level of trust when implementing data exploration for Remote Monitoring Services. Events occurring on the monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation. Moreover, trust-demanding services are implemented as smart contracts, to guarantee the required level of trustworthiness among participants. The approach is integrated with a tool for data exploration in the digital factory, and has been validated taking into account performances and cost requirements.

Mashups are agile applications that aggregate RESTful services, developed by third parties, whose functions are exposed as Web Application Program Interfaces (APIs) within public repositories. From mashups developers’ viewpoint, Web API search may benefit from selection criteria that combine several dimensions used to describe the APIs, such as categories, tags, and technical features (e.g., protocols and data formats). Nevertheless, other dimensions might be fruitfully exploited to support Web API search. Among them, past API usage experiences by other developers may be used to suggest the right APIs for a target application. Past experiences might emerge from the co-occurrence of Web APIs in the same mashups. Ratings assigned by developers after using the Web APIs to create their own mashups or after using mashups developed by others can be considered as well. This article aims to advance the current state of the art for Web API search and ranking from mashups developers’ point of view, by addressing two key issues: multi-dimensional modeling and multi-dimensional framework for selection. The model for Web API characterization embraces multiple descriptive dimensions, by considering several public repositories, that focus on different and only partially overlapping dimensions. The proposed Web API selection framework, called WISeR (Web apI Search and Ranking), is based on functions devoted to developers to exploit the multi-dimensional descriptions, in order to enhance the identification of candidate Web APIs to be proposed, according to the given requirements. Furthermore, WISeR adapts to changes that occur during the Web API selection and mashup development, by revising the dimensional attributes in order to conform to developers’ preferences and constraints. We also present an experimental evaluation of the framework.

Novel web application development techniques rely on selection of the right data services, and this task assumes relevance to properly support creativity of developers. To this aim, developers should not merely leverage service descriptions, that are often very simple, in terms of categories and (semantic) tags. Data service selection criteria should include the experience of other developers who used the services in the past for designing their own web applications. According to this viewpoint, a data service might be relevant since: (i) it has been already used in similar contexts (i.e., in applications designed with similar data services); (ii) it has been used by other developers whose service selection experiences are considered as relevant by the developer who is designing the new application. The importance given to past experiences of other developers might be qualified by exploring the social network that relates the developers themselves and its topology. In this paper, we test the effectiveness of a multi-layered model meant for the selection of data services for web application design, taking into account such a developers' network.

Nowadays web designers are able to perform fast development of web applications by aggregating Web APIs available in huge public repositories. This approach is referred to as web mashup. Web APIs may be searched and aggregated in different scenarios, namely the development of a new mashup, or the completion of an existing mashup, or the substitution of one or more Web APIs within it. Collective knowledge of other designers who used the same or similar Web APIs in their own mashups is of paramount relevance to support Web API selection. In this paper, we provide a framework of techniques based on such a collective knowledge to support Web API selection according to the different search patterns, which correspond to distinct mashup development scenarios. We also present a preliminary evaluation of the framework.

The design of Web applications from third-party Web APIs can be shortened by providing effective tools that abstract from heterogeneity of Web API descriptions and support the designer for their proactive selection. In this paper, we identify Web API selection patterns to support interactive and proactive Web application development according to an exploratory perspective. Selection patterns rely on a semantic characterization of Web API descriptions that abstracts from implementation details and semantics-enabled metrics for evaluation of coupling and similarity degree. A prototype tool that implements selection patterns is also presented.

In this paper we describe an approach for service discovery supported by semantic annotations. We propose a metamodel representation of both the WSDL documents and the associated semantic annotations. Based on this metamodel, effective service discovery is achieved by a Datalog engine implementing flexible matchmaking techniques that allow both exact and partial matches among search results. The metamodel is supported by a storage system that ensures scalability of the entire process. Finally we illustrate experiments on a public dataset of semantic service descriptions.

Mashup of components made available on the Web is gaining more and more interest as an opportunity to integrate contents and application logics from independent sources in new, short-living and situational applications. These applications are usually meant to fill immediate needs and should be built according to simplified composition paradigms. Although existing mashup frameworks, like IBM Lotus Mashup or Yahoo! Pipes, provide valid solutions that make it easy to compose new applications, designers often have to deal with large and heterogeneous component repositories. Components are manually selected and combined, still requiring a support for their efficient selection and integration. In this context, we propose to use semantic annotation of components apt to abstract from the heterogeneity of underlying APIs. Then, we describe techniques for building a mashup component repository where semantically annotated components are organized according to similarity and coupling links. Finally, we discuss interactive, computer-aided design of mashup applications.

In the recent years distributed architectures and P2P tech-nology have been adopted to better support effective col-laboration among networked organizations. According to the P2P paradigm, many autonomous peers need to coop-erate under highly dynamic conditions by sharing their re-sources (such as data and services), without sharing a com-mon semantic model or ontology and without having a-priori knowledge about each other. In such a scenario, for effective collaboration, new advanced tools are required to support se-mantic representation and discovery of distributed resources such as data and services. In this paper we focus on seman-tic collaboration and service discovery in P2P systems. We propose the construction of a service semantic overlay over the P2P network, that is, a dynamic conceptual map across peers that provide similar services and constitute synergic service centers.