Predictive process mining by network of classifiers and clusterers: the PEDF model
This work addresses the problem of predicting future events from system logs, which is relevant for system administrators and process managers, offering an incremental improvement over existing methods.
This paper proposes the PEDF model to predict future events in a system by learning from event logs, considering sequences, durations, and extra features. The model outperforms Recurrent Neural Network and Sequential Prediction models in its evaluations.
In this research, a model is proposed to learn from event log and predict future events of a system. The proposed PEDF model learns based on events' sequences, durations, and extra features. The PEDF model is built by a network made of standard clusterers and classifiers, and it has high flexibility to update the model iteratively. The model requires to extract two sets of data from log files i.e., transition differences, and cumulative features. The model has one layer of memory which means that each transition is dependent on both the current event and the previous event. To evaluate the performance of the proposed model, it is compared to the Recurrent Neural Network and Sequential Prediction models, and it outperforms them. Since there is missing performance measure for event log prediction models, three measures are proposed.