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Explaining the Impact of Data Characteristics on Process Mining Algorithms

MCML Authors

Andrea Maldonado

Dr.

* Former Member

→ Group Thomas Seidl
Database Systems, Data Mining and AI

Abstract

This dissertation develops a data-driven framework for robust evaluation in process mining, addressing the lack of standardized benchmarks and reliable assessment methods. It introduces structure-aware data characterization (DROPP, FEEED), bias-mitigating data generation frameworks (GEDI, iGEDI), and explainable experimentation using Shapley-value analysis to quantify how event data characteristics influence algorithm performance. Together, these contributions enable more reproducible, interpretable, and trustworthy evaluation of process mining methods across diverse datasets and scenarios (Shortened.)

phdthesis Mal25