Abstract Discovering relevant datasets in large, heterogeneous data ecosystems, such as Data Lakes or Data spaces, is a complex task, often hindered by a lack of transparency and user-centric explanations in the discovery process. Explainability is critical for enabling users to understand why specific datasets are recommended, what information they contain, and how they align with user-defined criteria and preferences. To address these challenges, this work proposes a novel Graph Retrieval-Augmented Generation (Graph RAG) framework to enhance explainability in a platform for discovery of summary data sources. The proposed approach leverages a Knowledge Graph (KG) to interpret user requests, extracting relevant contextual information. These enriched requests are then transformed by a Large Language Model (LLM) into actionable dataset queries for a dataset discovery platform. Candidate solutions are evaluated and enriched with statistical insights on value distributions and contextual knowledge from the KG. Finally, the LLM ranks these solutions based on user preferences, producing a final report. This dual strategy of query enrichment and contextual explanation fosters transparency and enhances user understanding of the discovery process. We demonstrate the effectiveness of the approach through an experimental validation, highlighting its potential to improve both the accuracy and interpretability of dataset discovery.

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.

Social networks are perceived by users as a natural environment for publicly sharing their thoughts and emotions about different subjects. In these platforms, despite the availability of various forms of communication, text is the most widespread way of communication. Therefore, the development of automatic techniques for emotion recognition in social media texts, such as tweets or Facebook posts, gives the opportunity of extracting information that could be valuable for many application fields, ranging from the analysis of customer satisfaction to the optimization of political campaigns. Nowadays, several emotion classifiers and datasets have been built for English texts while few resources are available for other languages. For this reason, in this work we present a deep learning algorithm for emotion recognition in Italian social media texts. The algorithm was named EmotionAlBERTo as it is based on AlBERTo, a BERT-based language understanding model for the Italian language. We trained and evaluated EmotionALBERTo on two different Twitter datasets: the MultiEmotions-It dataset and TwIT, a novel Italian dataset for emotion recognition that we built by collecting and manually labelling a corpus of about 3100 Italian tweets. Experiments show that the models achieve remarkable performance on both 4- class and 6-class emotion classification, by respectively obtaining F1= 0.91 and F1= 0.83 on MultiEmotions-It, F1= 0.92 and F1= 0.86 on TwIT.

The massive adoption of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions on a large variety of topics. Research has made many efforts in defining accurate algorithms for the analysis of emotions conveyed by texts, however their performance often relies on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually -engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

In the last years, the wide availability on the market of low-cost smart devices paved the way for the development of smart environments, which offer an unprecedented opportunity to recognize the patterns of activities from a large amount of collected data, with the ultimate aim of monitoring the user behavior. In this article, we propose a methodology which relies on process discovery techniques to analyze sensor data in terms of activation sequences and to discover process models representing user's behavioral patterns. The extraction of such models is valuable not only in the perspective of gaining a better insight on how a certain task is performed but also in supporting novel smart services. In order to evaluate the effectiveness of the approach, in this article, we also consider a real-world case study set in an ambient-assisted living environment.