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

arXiv: https://arxiv.org/abs/2411.02131 · HTML: https://mlsanity.com/paper/2411.02131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing](https://mlsanity.com/paper/1903.10145/) (2019)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Automated Discovery of Business Process Simulation Models from Event Logs](https://mlsanity.com/paper/1910.05404/) (2019)
- [An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction](https://mlsanity.com/paper/1811.00062/) (2018)
- [Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning](https://mlsanity.com/paper/2009.03567/) (2020)
- [Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models](https://mlsanity.com/paper/2303.17463/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining](https://mlsanity.com/paper/2610.17702/) (2026)

## Abstract (arXiv)

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