1 code implementation • 21 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.
1 code implementation • 12 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.
2 code implementations • 23 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.
no code implementations • 8 May 2018 • Ilya Verenich, Marlon Dumas, Marcello La Rosa, Fabrizio Maggi, Irene Teinemaa
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case.
no code implementations • 31 Oct 2018 • Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst
Data of sequential nature arise in many application domains in forms of, e. g. textual data, DNA sequences, and software execution traces.
1 code implementation • 23 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.
1 code implementation • 3 Sep 2020 • Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy
This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome.
1 code implementation • 15 May 2021 • Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy
The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process.
1 code implementation • 7 Mar 2023 • Zahra Dasht Bozorgi, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush, Irene Teinemaa
Increasing the success rate of a process, i. e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal.