A data-aware explainable deep learning approach for next activity prediction

The prediction of the next activity in a business process can be very useful in revealing inefficiencies and take decisions to avoid undesired activities. In this direction, further advantages can gather from the knowledge of the activities within their attributes that influence the occurrence of the predicted activity. This paper faces this issue by introducing a Data-aware Explainable Next Activity Prediction approach called DENAP based on the adoption of Long Short-Term Memory (LSTM) neural networks and Layer-Wise Relevance Propagation (LRP) method for the detection of next activity prediction in a business process and for the evaluation of the past activities and data that influence the prediction. The DENAP approach is validated on a set of synthetic and real logs. The obtained results show the good capability of DENAP to predict the next activity and indicate the more relevant activities/data with respect to the prediction.

Automated summary

This paper introduces a Data-aware Explainable Next Activity Prediction approach called DENAP based on the adoption of Long Short-Term Memory (LSTM) neural networks and Layer-Wise Relevance Propagation (LRP) method for the detection of next activity prediction in a business process and for the evaluation of the past activities and data that influence the prediction. The DENAP approach is validated on a set of synthetic and real logs. The obtained results show the good capability of DENAP to predict the next activity and indicate the more relevant activities/data with respect to the prediction.