  Process data with confidential information cannot be shared directly in
public, which hinders the research in process data mining and analytics. Data
encryption methods have been studied to protect the data, but they still may be
decrypted, which leads to individual identification. We experimented with
different models of representation learning and used the learned model to
generate synthetic process data. We introduced an adversarial generative
network for process data generation (ProcessGAN) with two Transformer networks
for the generator and the discriminator. We evaluated ProcessGAN and
traditional models on six real-world datasets, of which two are public and four
are collected in medical domains. We used statistical metrics and supervised
learning scores to evaluate the synthetic data. We also used process mining to
discover workflows for the authentic and synthetic datasets and had medical
experts evaluate the clinical applicability of the synthetic workflows. We
found that ProcessGAN outperformed traditional sequential models when trained
on small authentic datasets of complex processes. ProcessGAN better represented
the long-range dependencies between the activities, which is important for
complicated processes such as the medical processes. Traditional sequential
models performed better when trained on large data of simple processes. We
conclude that ProcessGAN can generate a large amount of sharable synthetic
process data indistinguishable from authentic data.
