Generating the Traces You Need: A Conditional Generative Model for Process Mining Data

TL;DR

This paper introduces a conditional generative model based on CVAE for process mining data, enabling controlled, targeted trace generation conditioned on control flow and temporal features, addressing limitations of existing deep learning models.

Contribution

The paper presents a novel CVAE-based approach for process data generation that allows conditioning on specific process attributes, improving control and variability in generated traces.

Findings

Effective generation of process traces conditioned on control flow and temporal features.

Enhanced control over generated data compared to previous models.

Evaluation metrics confirm the quality and variability of the generated traces.

Abstract

In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions to generate data samples based on specific conditions or attributes. This limitation is particularly significant because the ability to control the type of generated data can be beneficial in various contexts, enabling a focus on specific behaviours, exploration of infrequent patterns, or simulation of alternative 'what-if' scenarios. In this work, we address this challenge by introducing a conditional model for process data generation based on a conditional variational autoencoder (CVAE). Conditional models offer control over the generation process by tuning input conditional variables, enabling more targeted and controlled data generation. Unlike other domains, CVAE for process mining faces specific challenges due to the multiperspective nature of the data and the need to adhere to control-flow rules while ensuring data variability. Specifically, we focus on generating process executions conditioned on control flow and temporal features of the trace, allowing us to produce traces for specific, identified sub-processes. The generated traces are then evaluated using common metrics for generative model assessment, along with additional metrics to evaluate the quality of the conditional generation