Data-Driven Approaches Towards Transparent Benchmarking of Process Mining Tasks

MCML Authors

Andrea Maldonado

Dr.

* Former Member

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Abstract

The abundance of new approaches in process mining and the diversity of processes in the real-world, raises the question of this thesis: How can we create benchmarks, which reliably measure the impact of event data features on process mining evaluation? Developing benchmarks, that employ comprehensive intentional ED and also consider connections between ED characteristic features, methods, and metrics, will support process miners to evaluate methods more efficiently and reliably.

inproceedings Mal24

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Doctoral Consortium @ICPM 2024

Doctoral Consortium at the 6th International Conference on Process Mining. Lyngby, Denmark, Oct 14-18, 2024.

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Authors

A. Maldonado

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Links

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Research Area

 A3 | Computational Models