Controlled experiments in Process Mining primarily rely on synthetic event logs generated from declarative or procedural process modeling languages, which often lack the flexibility needed for precise experimental setups. In this paper, we introduce a novel log generator designed to address this gap by enabling fine-tuned customization of synthetic logs. The log generator, implemented using the declarative language Answer Set Programming (ASP), allows researchers to define sophisticated scenarios that are impossible to express with standard log generators to create specific experimental conditions. • Fine-grained and flexible event log generation with a new position-based modeling language ( PosLan ) and Answer-Set Programming. • Support for synthetic logs with characteristics that mimic complex real-world patterns. • Variability control of the generated event log via Answer-Set Programming.

This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based prior knowledge. While recent approaches leverage deep learning models for suffix prediction, they often fail to satisfy even basic logical constraints due to the absence of explicit integration of domain knowledge during training. We propose a novel method to incorporate Linear Temporal Logic over finite traces (LTLf) into the training process of autoregressive sequence predictors. Our approach introduces a differentiable logical loss function, defined using a soft approximation of LTLf semantics and the Gumbel-Softmax trick, which can be combined with standard predictive losses. This ensures the model learns to generate suffixes that are both accurate and logically consistent. Experimental evaluation on three real-world datasets shows that our method improves suffix prediction accuracy and compliance with temporal constraints. We also introduce two variants of the logic loss (local and global) and demonstrate their effectiveness under noisy and realistic settings. While developed in the context of BPM, our framework is applicable to any symbolic sequence generation task and contributes toward advancing Neuro-Symbolic AI.

This paper presents one of the first contributions in the context of Multimodal Predictive Process Monitoring (MM-PPM) . In recent years, Predictive Process Monitoring (PPM) has evolved at the intersection of process mining, machine learning, and data science, as organizations seek to anticipate the future course of ongoing processes. Traditional PPM mainly relies on structured event log data, but many real-world scenarios generate richer information, including text, images, audio, and video. MM-PPM promises to start addressing this rich data scenario by integrating complementary knowledge from heterogeneous modalities through modality-specific representations and information fusion techniques. The growing digitization of healthcare systems, combined with advances in Artificial Intelligence (AI), has accelerated AI-based PPM for analyzing sequences of clinical events, supporting decision-making, enabling personalized care, and improving clinical facility management. Given these characteristics, clinical pathways represent an ideal domain for experimenting with MM-PPM, as they may naturally involve diverse modalities such as structured records, free-text notes, or medical images. To handle multimodal information available with clinical pathways, we introduce MEDUSA , an MM-PPM approach for outcome prediction, which jointly processes medical image information coupled with the storytelling of structural records and text notes collected during the clinical pathway of a patient until the acquisition of the considered image. The evaluation of MEDUSA is done in a COVID-19 case study, to assess the performance of the proposed approach and explain how specific information within each modality influences the decisions of the predictive model.