Legal and administrative sources typically describe procedural steps that are not recorded in structured data, which makes the application of process-oriented analysis particularly challenging. In this work, we present an approach that uses Large Language Models (LLMs) to extract events and dates from unstructured legal texts. The methodology is applied to a dataset of Italian procurement notices published since 2022 on the official EU platform, Tenders Electronic Daily (TED), demonstrating how LLMs can extract valuable information, such as administrative decisions adopted prior to tender publication. These elements are incorporated into existing event logs, thereby enhancing the quality of process analysis. A sample of the extracted data has been manually reviewed by legal experts to assess the relevance and correctness of the automated detection. The results suggest that this approach can help identify procedural steps hidden in free text, thereby supporting more complete and accurate representations of legal workflows.

Abstract The rise of e-learning systems has revolutionized education, enabling the collection of valuable students’ activity data for continuous improvement. While existing studies have predominantly focused on prolonged learning paths, short-term tutorials offer a flexible and efficient alternative that is recently gaining increasing popularity. This article presents a methodology for investigating e-learning systems for short-term tutorials leveraging user behavior tracking and process mining techniques. A case study involving a web-based tutorial with approximately one hour of learning explores the learning processes of 250 students in Italy. The study analyzes learning outcomes and investigates the impact of different learning paths on student progress. The research questions concern i) the extraction of activity flows in short-term tutorials; ii) the prediction of outcomes in the early stages of short-term learning process. The proposed approach provides descriptive insights into the learning process which can also be used to offer prescriptive guidance.

This paper introduces an experiment and the first results of a research on computer programming education using process mining methods. A web-based tutorial addresses the topic of agent-based modeling by introducing a guided exercise with NetLogo, a widely used tool for modeling natural and social phenomena. A goal of the project is to analyze the goodness of the learning process, also through appropriate tests placed between the pages and at the end of the tutorial. Actual data extracted on student behavior (e.g., length of time spent on different parts of each web page, movements on the page, mouse position and mouse clicks) are examined using process discovery technique. Special attention is given to the return of student learning outcomes through visualization. Our solution includes heatmaps and direct-follow graphs of the real processes. Initial results are encouraging on the possibility of improving the assessment of learning processes by relying on techniques from the discipline of process mining, as shown by the case of web-based behaviour tracking data.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

We introduce an agent-based framework (developed in NetLogo, one of most relevant simulation platforms) to simulate the diffusion of a piece of misinformation, according to a known compartmental model in which the fake news and its debunking compete in a social network. The tool allows to set different values for the spreading rate of the news, the hoax credibility, the probability of fact-checking and the forgetting rate of the agents. Moreover, it is possible to run the process over any given network. Since NetLogo is free and open source, our tool could be easily used and/or personalised by other researchers to explore different scenarios of fake news spreading.

This article focuses on the analysis of the admissions to hospital-at-home service within the framework of Business Process Management. While traditional process analysis deal with internal hospital services, having a particular and specific scenario, e.g. a ward of an hospital, here we investigate a quite innovative service with a strong socio-territorial impact based on real data. In particular, we are interested in the understanding of the selection process in which staff discriminate cases of interest for the service. We describe here our methodological framework combining data and event log analysis, modeling with standard language and business process simulation with scenario analysis.

Risk management in business process is a key factor of success for organization as risks are part of every business activity.Errors may bring to increased costs, loss of quality as well as time delays, which in healthcare can bring to serious damages.This paper proposes a methodological framework to investigate risks in organizations by adopting a Business Process Management perspective that includes modeling and simulation of business processes.We applied our methodology to processes in the Blood Bank department of a large hospital.Our results show that a simulation-driven approach is an effective way to intercept and estimate real risks and to provide a decision support to guide the of department's managers.1 Cfr.

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media.