The EU H2020 project social cohesion, participation, and inclusion through cultural engagement (SPICE) focuses on developing, designing, and implementing new methods and digital tools for citizen curation. This article delineates several software tools developed within the project, presenting innovative approaches to represent and visualize citizens and communities resulting from their engagement with cultural heritage. Aligned with the central tenets of SPICE –particularly the notions of belonging and the interpretation reflection loop– the primary objective is to bolster citizens’ participation and inclusion in fostering social cohesion. This article describes how the SPICE tools can be utilized to guide the processes of interpretation and reflection on cultural heritage artifacts. The Community Model serves as a pivotal component, enabling the modeling of citizens and communities through the utilization of similarity functions for clustering citizens based on perspectives. The clustering algorithm is intricately crafted to generate coherent communities, iterating until all clusters are interpretable using demographic attributes, centroid-based representations, and similarity attributes. Authors posit that this model holds significant value in comprehending and structuring complex data within cultural heritage contexts. To exemplify our approach, the article examines different attributes of individual citizens and citizen groups in the Galleria Civica d’Arte Moderna e Contemporanea (GAM) case study. Here, perspectives are delineated based on visitors’ demographic attributes and their emotional responses when engaging with artworks. These perspectives are then visualized using the VISIR tool, facilitating the exploration and revelation of connections between citizens and communities, thereby bridging the realms of citizen space and cultural heritage space.

The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the “macro-activities” subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach.

Clinical guidelines, which represent the normative process models for healthcare organizations, are typically available in a textual, unstructured form. This issue hampers the application of classical conformance-checking algorithms to the medical domain, which take in input of a formalized and computer-interpretable description of the process. In this paper, (i) we propose overcoming this problem by taking advantage of a Large Language Model (LLM), in order to extract normative rules from textual guidelines; (ii) we then check and quantify the conformance of the patient event log with respect to such rules. Additionally, (iii) we adopt the approach as a means for evaluating the quality of the models mined by different process discovery algorithms from the event log, by comparing their conformance to the rules. We have tested our work in the domain of stroke. As regards conformance checking, we have proved the compliance of four Northern Italy hospitals to a general rule for diagnosis timing and to two rules that refer to thrombolysis treatment, and have identified some issues related to other rules, which involve the availability of magnetic resonance instruments. As regards process model discovery evaluation, we have assessed the superiority of Heuristic Miner with respect to other mining algorithms on our dataset. It is worth noting that the easy extraction of rules in our LLM-assisted approach would make it quickly applicable to other fields as well.

Microbiome research advancements have provided countless insights. Despite the massive amount of data currently stored in public repositories, these resources remain vastly underutilized due to the intricacy of data and metadata retrieval from these databases. However, leveraging data-driven approaches is crucial for microbiome research progress by overcoming variations between studies and identifying generalizable trends. We designed the open-access and user-friendly bioinformatic tool MADAME (MetADAta MicrobiomE) to streamline the data and metadata retrieval process. MADAME addresses the challenges posed by the public repositories’ current limitations, allowing users to retrieve publications associated with the accession codes of interest. Additionally, MADAME allows users to visually explore retrieved results through the generation of a comprehensive report with plots and statistics. These unique features of MADAME let users maximize their time and resources, enabling them to assess metadata suitability before pursuing data download. To showcase its diverse functionalities, we recreate several scenarios to meet the diverse requirements that researchers may have.