2 citations · 2 across the 4 of their papers we have counts for
6 papers · 1 filter
Actor-Enriched Time Series Forecasting of Process Performance
Aurelie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is…
Linking Actor Behavior to Process Performance Over Time
Aurélie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Understanding how actor behavior influences process outcomes is a critical aspect of process mining. Traditional approaches often use aggregate and static process data, overlooking…
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning
Zahra Dasht Bozorgi, Marlon Dumas, Marcello La Rosa +3
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. At runtime, there are often certain…
Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction
Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas +2
Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of…
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas +2
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…
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction
Farbod Taymouri, Marcello La Rosa, Sarah Erfani +2
Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process m…