Search Results for author: Fabrizio Maria Maggi

Found 20 papers, 9 papers with code

Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring

1 code implementation18 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.

Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction

1 code implementation14 Dec 2023 Ivan Donadello, Jonghyeon Ko, Fabrizio Maria Maggi, Jan Mendling, Francesco Riva, Matthias Weidlich

Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion.

Activity Prediction Predictive Process Monitoring

Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles

no code implementations27 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.

Predictive Process Monitoring

Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns

2 code implementations9 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.

Nirdizati: an Advanced Predictive Process Monitoring Toolkit

no code implementations18 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.

Predictive Process Monitoring

ASP-Based Declarative Process Mining (Extended Abstract)

1 code implementation4 May 2022 Francesco Chiariello, Fabrizio Maria Maggi, Fabio Patrizi

We propose Answer Set Programming (ASP) as an approach for modeling and solving problems from the area of Declarative Process Mining (DPM).

Explainable Predictive Process Monitoring: A User Evaluation

no code implementations15 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.

BIG-bench Machine Learning Decision Making +2

Monitoring Hybrid Process Specifications with Conflict Management: The Automata-theoretic Approach

no code implementations25 Nov 2021 Anti Alman, Fabrizio Maria Maggi, Marco Montali, Fabio Patrizi, Andrey Rivkin

For example, in the medical domain, a clinical guideline describing the treatment of a specific disease cannot account for all possible co-factors that can coexist for a specific patient and therefore additional constraints may need to be considered.

Management

Exploring Business Process Deviance with Sequential and Declarative Patterns

no code implementations24 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.

Attribute

Process discovery on deviant traces and other stranger things

no code implementations30 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.

How do I update my model? On the resilience of Predictive Process Monitoring models to change

no code implementations8 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.

Incremental Learning Predictive Process Monitoring

Automated Discovery of Data Transformations for Robotic Process Automation

no code implementations3 Jan 2020 Volodymyr Leno, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Artem Polyvyanyy

In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another.

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

1 code implementation23 May 2019 Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.

Predictive Process Monitoring

Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

no code implementations31 Jul 2018 Diego Calvanese, Marlon Dumas, Fabrizio Maria Maggi, Marco Montali

The Decision Model and Notation (DMN) is a recent OMG standard for the elicitation and representation of decision models, and for managing their interconnection with business processes.

Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments

no code implementations11 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.

Incremental Learning Predictive Process Monitoring

Alarm-Based Prescriptive Process Monitoring

2 code implementations23 Mar 2018 Irene Teinemaa, Niek Tax, Massimiliano de Leoni, Marlon Dumas, Fabrizio Maria Maggi

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof.

Predictive Process Monitoring

Temporal Stability in Predictive Process Monitoring

1 code implementation12 Dec 2017 Irene Teinemaa, Marlon Dumas, Anna Leontjeva, Fabrizio Maria Maggi

We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability.

Binary Classification Predictive Process Monitoring +2

Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

1 code implementation21 Jul 2017 Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces.

Predictive Process Monitoring

Business Process Deviance Mining: Review and Evaluation

1 code implementation29 Aug 2016 Hoang Nguyen, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Suriadi Suriadi

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 its expected or desirable outcomes.

General Classification

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