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.

The Internet of Services paradigm promotes using data services to accomplish diverse analytics tasks, enhancing collaboration amongst the actors of a production network. While domain experts and R&D managers are familiar with the observed system and can identify the data needed for analytics and digital innovation purposes, IT specialists typically handle data service discovery and their combination into analytics pipelines. Recently, Large Language Models (LLMs) have been recognised as valuable tools to support domain experts and R&D managers in specifying service needs and designing preliminary analytics pipelines, which can then be implemented by IT specialists, thus bridging the gap between domain and technical expertise. However, constructing effective prompts tailored to domain experts for interacting with LLM-based systems still demands advanced technical skills, as well as extensive knowledge of the catalog of available services and how they can be combined into analytics pipelines. To address this challenge, we propose an LLM-based approach for data service discovery within the Internet of Production context, which leverages Retrieval-Augmented Generation (RAG) applied to a catalog of atomic data services and relevant analytics pipelines. Preliminary experiments evaluating the effectiveness of the approach were conducted in a real-world case study within a Smart Factory research project.

In the last decades, the rise of Big Data solutions has significantly advanced the analysis of time series data as representation of dynamic phenomena through sequences of observations. Recent research efforts have advocated for the adoption of data summarisation techniques, such as incremental clustering, to promptly capture data evolution, thus facilitating domain experts in making informed and proactive decisions, capitalising on a compact representation of time series. Neverthe-less, while incremental clustering effectively reduces data volume, thus preserving relevant statistical information, it is crucial to estimate the degree of approximation between the original time series data and its summarised version. This evaluation is pivotal whenever the summarisation output is the starting point to set up complex analytical pipelines (e.g., for pattern recognition and anomaly detection purposes). Stemming from practical and empirical considerations made upon both a synthetic and a real-world dataset, we propose in this paper a variant of a renowned quality metric for incremental clustering, to assess the extent to which the time series summary accurately captures the dynamics of the original data.

Monitoring business processes within complex supply chains demands efficient data collection and analytics tailored to diverse phenomena. Traditional centralized solutions face limitations in adapting to the dynamic nature of supply chains. This calls for distributed solutions which break the usual architectural assumption to have a central entity in charge of collecting, integrating and offering tools for the analysis. This project, embedded in a larger initiative called MICS, proposes an inno-vative distributed monitoring solution integrating blockchain for a trustworthy and efficient data analytics strategy that preserves data sovereignty in complex collaborative environments. Leveraging the cloud -edge continuum, the solution aims to ensure secure data exchange, adherence to agreements, and real-time analytics. Expected outcomes include an innovative federated architecture, 5G slice management solutions, an adversarial analysis of supply chain security, and a proof-of-concept implementation of the blockchain-based data flow tracking system. These developments aim to enhance the reliability, security, and efficiency of supply chain monitoring in dynamic industrial environments.

In the context of the Internet of Services paradigm for Industry 4.0, data services can be discovered and composed to accomplish different data analytics scenarios amongst the actors of a production network. Recently, Large Language Models (LLMs) have been increasingly considered for service discovery and composition as a promising alternative to previous approaches that often require substantial effort to produce formal service descriptions and/or annotations. In this paper, we introduce some exploratory experiments on the use of an LLM-based system for the discovery of data services to fulfil data analysis scenarios. First, a data service model, that represents in a declarative way data service operations, is provided. Then, we propose prompt templates for the interaction with the LLM-based system, that leverages the service model, aimed at reducing trial-and-error interactions for identifying potential service candidates. The effectiveness of the approach is being assessed in a real-world case study of a research project.

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.

The implementation of remote services in the factory of the future is gaining more and more attention. A distributed process can be considered, where an Original Equipment Manufacturer supplies services based on data collected from the machines of its clients. Examples of services are those implementing anomaly detection and predictive maintenance techniques. Data on which remote services are based, as well as their execution logic, must be transparently shared among participants, to enforce trust among OEM and its clients. The goal of this paper is to propose an approach where blockchain technology and Smart Contracts are used to ensure the required level of trust when implementing the aforementioned services. Events occurring on monitored machines are stored as transactions in a blockchain-based system, to ensure non repudiation of data on which remote services are based. Moreover, trust-demanding tasks in the execution logic of services are considered to design Smart Contracts, that guarantee the required level of trustworthiness among participants. The example featuring Remote Monitoring Services for anomaly detection is used to demonstrate the feasibility of the approach.

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.

Ignited by the advent of Data Science, organisations are spending more and more resources in understanding their Big Data, attracted by the opportunity of turning them into actionable insights. Data Lakes have been proposed as repositories in charge of storing vast amount of heterogeneous data, regardless its structure, enabling the possibility of postponing transformation and analytical processes. In this context, Semantic Web technologies may be used to enable interoperability and improve data access, by providing Data Exploration Services. Starting from these premises, the goal of this paper is to describe a semantic approach apt to the compelling challenge of Data Exploration Services, aimed at personalising the exploration experience. The approach has been preliminary validated within a Smart City context, where aggregation of urban data, according to multiple perspectives through the definition of proper indicators, enables urban data exploration at different granularity levels for distinct categories of users.