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IGEDI: Interactive Generating Event Data With Intentional Features

MCML Authors

Link to Profile Thomas Seidl

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

Process mining solutions aim to improve performance, save resources, and address bottlenecks in organizations. However, success depends on data quality and availability, and existing analyses often lack diverse data for rigorous testing. To overcome this, we propose an interactive web application tool, extending the GEDI Python framework, which creates event datasets that meet specific (meta-)features. It provides diverse benchmark event data by exploring new regions within the feature space, enhancing the range and quality of process mining analyses. This tool improves evaluation quality and helps uncover correlations between meta-features and metrics, ultimately enhancing solution effectiveness.

inproceedings MAF+24

Demo Tracks @ICPM 2024

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

Authors

A. Maldonado • S. A. Aryasomayajula • C. M. M. Frey • T. Seidl

Links

URL

Research Area

 A3 | Computational Models

BibTeXKey: MAF+24

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