35 citations · 56 across the 13 of their papers we have counts for
13 papers
Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs
Alessandro Padella, Massimiliano de Leoni, Marlon Dumas
Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures.…
An Experimental Comparison of Alternative Techniques for Event-Log Augmentation
Alessandro Padella, Francesco Vinci, Massimiliano de Leoni
Process mining analyzes and improves processes by examining transactional data stored in event logs, which record sequences of events with timestamps. However, the effectiveness of…
Accurate and Noise-Tolerant Extraction of Routine Logs in Robotic Process Automation (Extended Version)
Massimiliano de Leoni, Faizan Ahmed Khan, Simone Agostinelli
Robotic Process Mining focuses on the identification of the routine types performed by human resources through a User Interface. The ultimate goal is to discover routine-type model…
Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)
Francesco Vinci, Gyunam Park, Wil van der Aalst +1
Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discove…
Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)
Massimiliano de Leoni, Alessandro Padella
Predictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform un…
What if Process Predictions are not followed by Good Recommendations? (Technical Report)
Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst +1
Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to redu…