Nine years later: Reflecting on our article

Information Systems, vol. 137, pp. 102644

Abstract

This contribution revisits our article titled “A General Process Mining Framework for Correlating, Predicting, and Clustering Dynamic Behavior Based on Event Logs” accepted from 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.

Authors 3

1. Massimiliano de Leoni corresponding

   University of Padua

   Affiliation as printed

   Department of Mathematics, University of Padova, Padua, Italy

2. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

3. Marcus Dees

   Radboud University Medical Center

   Affiliation as printed

   Radboud University Medical Center (Radboudumc), Nijmegen, The Netherlands