1 code implementation • 18 Mar 2024 • Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Ivan Donadello, Fabrizio Maria Maggi
In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime.
no code implementations • 27 Mar 2023 • Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi
Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions.
no code implementations • 16 Mar 2023 • Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Francesca Meneghello, Massimiliano Ronzani
Prescriptive Process Monitoring is a prominent problem in Process Mining, which consists in identifying a set of actions to be recommended with the goal of optimising a target measure of interest or Key Performance Indicator (KPI).
2 code implementations • 9 Nov 2022 • Ivan Donadello, Chiara Di Francescomarino, Fabrizio Maria Maggi, Francesco Ricci, Aladdin Shikhizada
Such encoded log is used to train a Machine Learning classifier to learn a mapping between the temporal patterns and the outcome of a process execution.
no code implementations • 18 Oct 2022 • Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi
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 executions recorded in event logs.
no code implementations • 29 Mar 2022 • Stefano Branchi, Chiara Di Francescomarino, Chiara Ghidini, David Massimo, Francesco Ricci, Massimiliano Ronzani
The rise of process data availability has recently led to the development of data-driven learning approaches.
no code implementations • 9 Mar 2022 • Marco Roveri, Claudio Di Ciccio, Chiara Di Francescomarino, Chiara Ghidini
In this paper, we investigate the problem of extracting the unsatisfiable core in LTLf specifications.
no code implementations • 15 Feb 2022 • Williams Rizzi, Marco Comuzzi, Chiara Di Francescomarino, Chiara Ghidini, Suhwan Lee, Fabrizio Maria Maggi, Alexander Nolte
The results of the user evaluation show that, although explanation plots are overall understandable and useful for decision making tasks for Business Process Management users -- with and without experience in Machine Learning -- differences exist in the comprehension and usage of different plots, as well as in the way users with different Machine Learning expertise understand and use them.
no code implementations • 24 Nov 2021 • Giacomo Bergami, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Joonas Puura
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 or desirable outcomes.
no code implementations • 30 Sep 2021 • Federico Chesani, Chiara Di Francescomarino, Chiara Ghidini, Daniela Loreti, Fabrizio Maria Maggi, Paola Mello, Marco Montali, Sergio Tessaris
As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative.
no code implementations • 8 Sep 2021 • Williams Rizzi, Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi
Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution.
no code implementations • 27 Sep 2019 • Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris
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.
no code implementations • 11 Apr 2018 • Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Williams Rizzi, Cosimo Damiano Persia
The results provide a first evidence of the potential of incremental learning strategies for predicting process monitoring in real environments, and of the impact of different case encoding strategies in this setting.
no code implementations • 6 Apr 2018 • Chiara Di Francescomarino, Chiara Ghidini, Fabrizio Maria Maggi, Fredrik Milani
Predictive process monitoring has recently gained traction in academia and is maturing also in companies.
no code implementations • 1 Jun 2017 • Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Sergio Tessaris
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 more complex forms of process analysis.
no code implementations • 17 Jun 2016 • Federico Chesani, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Paola Mello, Marco Montali, Sergio Tessaris
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 assessment of the goodness of a process model.