Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks
This work addresses the problem of predictive process analytics for business process management, offering an incremental improvement in representation learning for event logs.
The paper tackles the challenge of learning meaningful representations from diverse business process data by proposing a novel approach that combines Gramian Angular Fields and Convolutional Neural Networks, demonstrating effectiveness in tasks like case retrieval and process prediction compared to existing methods.
Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or computer vision. Instead of training a model for one specific task, representation learning is about training a model to capture all useful information in the underlying data and make it accessible for a predictor. For predictive process analytics, it is essential to have all explanatory characteristics of a process instance available when making predictions about the future, as well as for clustering and anomaly detection. Due to the large variety of perspectives and types within business process data, generating a good representation is a challenging task. In this paper, we propose a novel approach for representation learning of business process instances which can process and combine most perspectives in an event log. In conjunction with a self-supervised pre-training method, we show the capabilities of the approach through a visualization of the representation space and case retrieval. Furthermore, the pre-trained model is fine-tuned to multiple process prediction tasks and demonstrates its effectiveness in comparison with existing approaches.