Graph-Based Event Log Repair

The quality of event logs in Process Mining is crucial when applying any form of analysis to them. In real-world event logs, the acquisition of data can be non-trivial (e.g., due to the execution of manual activities and related manual recording or to issues in collecting event attributes), and often may end up with events recorded with some missing information. Standard approaches to the problem of trace (or log) reconstruction either require the availability of a process model that is used to fill missing values by leveraging different reasoning techniques or employ a Machine Learning/Deep Learning model to restore the missing values by learning from similar cases. In recent years, a new type of Deep Learning model that is capable of handling input data encoded as graphs has emerged, namely Graph Neural Networks. Graph Neural Network models, and even more so Heterogeneous Graph Neural Networks, offer the advantage of working with a more natural representation of complex multi-modal sequences like the execution traces in Process Mining. In this work, we focus on the development of a Heterogeneous Graph Neural Network model that, given a trace containing some incomplete events, returns the full set of attributes missing from those events. We evaluate our work against a state-of-the-art approach that leverages autoencoders on two synthetic logs and four real event logs, with different types of missing values. The evaluation results show that our method robustly achieves higher accuracy than the autoencoder approach.