This contribution revisits our article titled "A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs" , published in the Information Systems journal in 2016. It reflects on how the proposed general framework for process mining has grown in relevance with the rise of AI, emphasizing its value as a extensible approach to transforming event data into analytical and predictive insights. It also discusses how the framework relevance and the underlying message remains valid, including for emerging research directions such as prescriptive analytics, causal and/or object-centric process mining.

Nine years later: Reflecting on our article: A general process mining framework for correlating, predicting, and clustering dynamic behavior based on event logs

de Leoni, Massimiliano
;
2026

Abstract

This contribution revisits our article titled "A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs" , published in the Information Systems journal in 2016. It reflects on how the proposed general framework for process mining has grown in relevance with the rise of AI, emphasizing its value as a extensible approach to transforming event data into analytical and predictive insights. It also discusses how the framework relevance and the underlying message remains valid, including for emerging research directions such as prescriptive analytics, causal and/or object-centric process mining.
2026
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3587541
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