Search Results for author: Irene Teinemaa

Found 8 papers, 6 papers with code

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

1 code implementation15 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.

Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs

1 code implementation3 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.

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

An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction

no code implementations31 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.

Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring

no code implementations8 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.

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.

Predictive Process Monitoring Time Series

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

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