Synthetic Training-Data Generation for ML-based Process Mining Tools

This work addresses the challenge of data scarcity in process mining by proposing the creation of synthetic training data using generative models. A comparative analysis is conducted between a Long Short-Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model, using two distinct...

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Bibliographic Details
Published in2024 14th International Conference on Advanced Computer Information Technologies (ACIT) pp. 705 - 709
Main Authors Singh, Anjali, Bettouche, Zineddine, Fischer, Andreas
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.09.2024
Subjects
Analytical models
Data models
Data structures
event logs
Generative adversarial networks
generative adversarial networks (GAN)
Long short term memory
long short-term memory (LSTM) networks
Process mining
Reliability
Synthetic data
synthetic data generation
synthetic log generation
Training
Training data
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Summary:This work addresses the challenge of data scarcity in process mining by proposing the creation of synthetic training data using generative models. A comparative analysis is conducted between a Long Short-Term Memory (LSTM) model and the Generative Adversarial Network (GAN) model, using two distinct datasets. Multiple evaluation methods are employed to compare the results from the two models based on: precision, fidelity, diversity, and novelty. Results indicate that while LSTM accurately reproduces the initial data structure, GAN introduces more variability, offering a wider range of training scenarios. This highlights the potential of GAN-generated data to enhance the effectiveness and reliability of machine learning-based process mining tools
ISSN:2770-5226
DOI:10.1109/ACIT62333.2024.10712516