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CC-HIT: Creating Counterfactuals From High-Impact Transitions

MCML Authors

Link to Profile Thomas Seidl

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

Reliable process information, especially regarding trace durations, is crucial for smooth execution. Without it, maintaining a process becomes costly. While many predictive systems aim to identify inefficiencies, they often focus on individual process instances, missing the global perspective. It is essential not only to detect where delays occur but also to pinpoint specific activity transitions causing them. To address this, we propose CC-HIT (Creating Counterfactuals from High-Impact Transitions), which identifies temporal dependencies across the entire process. By focusing on activity transitions, we provide deeper insights into relational impacts, enabling faster resolution of inefficiencies. CC-HIT highlights the most influential transitions on process performance, offering actionable insights for optimization. We validate this method using the BPIC 2020 dataset, demonstrating its effectiveness compared to existing approaches.

inproceedings XZT+24

ML4PM @ICPM 2024

5th International Workshop on Leveraging Machine Learning in Process Mining at the 6th International Conference on Process Mining. Lyngby, Denmark, Oct 14-18, 2024.

Authors

Z. Xian • L. Zellner • G. M. Tavares • T. Seidl

Links

DOI

Research Area

 A3 | Computational Models

BibTeXKey: XZT+24

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