Marcus Dees

21 papers A 1B 4Journal 9Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
Enhancing explainability in process variant analysis: a framework for detecting and interpreting control-flow changes.
Softw. Syst. Model.
Ali Norouzifar, Majid Rafiei, Marcus Dees, Wil M. P. van der Aalst
2026 J jnl
Nine years later: Reflecting on our article: A general process mining framework for correlating, predicting, and clustering dynamic behavior based on event logs.
Inf. Syst.
Massimiliano de Leoni, Wil M. P. van der Aalst, Marcus Dees
2026 J jnl
Rule-guided process discovery.
Data Knowl. Eng.
Ali Norouzifar, Marcus Dees, Wil M. P. van der Aalst
2025 B conf
Decision Noise Instrument (DNI): Estimating Decision Noise in Business Processes.
EDOC
Marcus Dees, P. H. G. Berkhout, Claudio Di Ciccio, Hajo A. Reijers
2025 J jnl
Integrating Domain Knowledge into Process Discovery Using Large Language Models.
CoRR
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil M. P. van der Aalst
2024 — conf
Bridging Domain Knowledge and Process Discovery Using Large Language Models.
Business Process Management Workshops
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil M. P. van der Aalst
2024 J jnl
Bridging Domain Knowledge and Process Discovery Using Large Language Models.
CoRR
Ali Norouzifar, Humam Kourani, Marcus Dees, Wil M. P. van der Aalst
2024 B conf
Event Log Extraction for Process Mining Using Large Language Models.
CoopIS
Vinicius Stein Dani, Marcus Dees, Henrik Leopold, Kiran Busch, Iris Beerepoot, Jan Martijn E. M. van der Werf, Hajo A. Reijers
2024 — conf
Imposing Rules in Process Discovery: An Inductive Mining Approach.
RCIS (1)
Ali Norouzifar, Marcus Dees, Wil M. P. van der Aalst
2024 J jnl
Imposing Rules in Process Discovery: an Inductive Mining Approach.
CoRR
Ali Norouzifar, Marcus Dees, Wil M. P. van der Aalst
2024 — conf
Process Variant Analysis Across Continuous Features: A Novel Framework.
BPMDS/EMMSAD@CAiSE
Ali Norouzifar, Majid Rafiei, Marcus Dees, Wil M. P. van der Aalst
2024 J jnl
Process Variant Analysis Across Continuous Features: A Novel Framework.
CoRR
Ali Norouzifar, Majid Rafiei, Marcus Dees, Wil M. P. van der Aalst
2023 — conf
Generating Process Anomalies with Markov Chains: A Pattern-Driven Approach.
ICPM Workshops
Jochem Veldman, Xixi Lu, Wouter van der Waal, Marcus Dees, Inge van de Weerd
2021 — ch.
Accurate Predictions, Invalid Recommendations: Lessons Learned at the Dutch Social Security Institute UWV.
Business Process Management Cases (2)
Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst, Hajo A. Reijers
2020 B conf
Design and Evaluation of a Process-aware Recommender System based on Prescriptive Analytics.
ICPM
Massimiliano de Leoni, Marcus Dees, Laurens Reulink
2020 B conf
Events Put into Context (EPiC).
ICPM
Marcus Dees, Bart Hompes, Wil M. P. van der Aalst
2019 J jnl
What if Process Predictions are not followed by Good Recommendations?
CoRR
Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst, Hajo A. Reijers
2019 — conf
What if process predictions are not followed by good recommendations?
BPM (Industry Forum)
Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst, Hajo A. Reijers
2017 — conf
Enhancing Process Models to Improve Business Performance: A Methodology and Case Studies.
OTM Conferences (1)
Marcus Dees, Massimiliano de Leoni, Felix Mannhardt
2016 J jnl
A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs.
Inf. Syst.
Massimiliano de Leoni, Wil M. P. van der Aalst, Marcus Dees
2014 A conf
A General Framework for Correlating Business Process Characteristics.
BPM
Massimiliano de Leoni, Wil M. P. van der Aalst, Marcus Dees