Graph-based Event Log Repair
Sebastiano Dissegna, Chiara Di Francescomarino, Massimiliano Ronzani

TL;DR
This paper introduces a Heterogeneous Graph Neural Network model for repairing incomplete event logs in Process Mining, effectively reconstructing missing event attributes using graph-based deep learning techniques.
Contribution
It presents a novel GNN-based approach for comprehensive event log repair, outperforming existing autoencoder methods in reconstructing all event attributes.
Findings
Effective reconstruction of missing event attributes
Outperforms state-of-the-art autoencoder approaches
Works well on both synthetic and real logs
Abstract
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, for each event, all its 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…
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