286 citations · 577 across the 19 of their papers we have counts for
12 papers · 1 filter
Graph-based Event Log Repair
Sebastiano Dissegna, Chiara Di Francescomarino, Massimiliano Ronzani
The quality of event logs in Process Mining is crucial when applying any form of analysis to them. In real-world event logs, the acquisition of data can be non-trivial (e.g., due t…
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini +2
Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field…
Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches
Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino +4
Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes. Most existing works prescribe pre-defi…
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Ivan Donadello, Chiara Di Francescomarino, Fabrizio Maria Maggi +2
Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such inte…
Nirdizati: an Advanced Predictive Process Monitoring Toolkit
Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini +1
Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process exec…
Explainable Predictive Process Monitoring: A User Evaluation
Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino +4
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also…
Exploring Business Process Deviance with Sequential and Declarative Patterns
Giacomo Bergami, Chiara Di Francescomarino, Chiara Ghidini +2
Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to {their} expected…
Solving reachability problems on data-aware workflows
Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini +1
Recent advances in the field of Business Process Management have brought about several suites able to model complex data objects along with the traditional control flow perspective…
Predictive Process Monitoring Methods: Which One Suits Me Best?
Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi +1
Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for compa…
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi +2
A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the futu…
Enhancing workflow-nets with data for trace completion
Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini +1
The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the scientific investigation towards techniques and tools which support mo…
Abducing Compliance of Incomplete Event Logs
Federico Chesani, Riccardo De Masellis, Chiara Di Francescomarino +4
The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the ass…