1. Generating the Traces You Need: A Conditional Generative Model for Process Mining Data
- Author
-
Graziosi, Riccardo, Ronzani, Massimiliano, Buliga, Andrei, Di Francescomarino, Chiara, Folino, Francesco, Ghidini, Chiara, Meneghello, Francesca, and Pontieri, Luigi
- Subjects
Computer Science - Databases ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - 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, Comment: 6th International Conference on Process Mining (ICPM) 2024 Copenhagen, Denmark 14-18 October 2024
- Published
- 2024