57 citations · 99 across the 10 of their papers we have counts for
12 papers
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…
Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa +4
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 outc…
An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction
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. Different research disciplines have develo…
Automatic Playlist Continuation through a Composition of Collaborative Filters
Irene Teinemaa, Niek Tax, Carlos Bentes
The RecSys Challenge 2018 focused on automatic playlist continuation, i.e., the task was to recommend additional music tracks for playlists based on the playlist's title and/or a s…