PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks
This addresses the need for accurate process forecasting in business process management, though it appears incremental as it applies existing deep learning methods to a new task.
The paper tackled the problem of predicting future event logs in process mining by proposing a sequence-to-sequence deep learning approach, achieving perfect predictions on synthetic logs and demonstrating potential for real-world applications.
Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management. Process forecasting is a sub-field of process mining that studies how to predict future processes and process models. In this paper, we introduce and motivate the problem of event log prediction and present our approach to solving the event log prediction problem, in particular, using the sequence-to-sequence deep learning approach. We evaluate and analyze the prediction outcomes on a variety of synthetic logs and seven real-life logs and show that our approach can generate perfect predictions on synthetic logs and that deep learning techniques have the potential to be applied in real-world event log prediction tasks. We further provide practical recommendations for event log predictions grounded in the outcomes of the conducted experiments.