HAM-Net: Predictive Business Process Monitoring with a hierarchical attention mechanism

One of the essential tasks in Business Process Management (BPM) is Predictive Business Process Monitoring. This task aims to predict the behavior of an ongoing process based on the historical data stored in event logs. Since feed-forward neural networks do not consider the order of events for the prediction, they may not be helpful in predictive process monitoring. Recent research shows that using Recurrent Neural Networks such as LSTM and GRU may not be also helpful in predictive process monitoring. Because these networks use only the last hidden state as the context vector, and may lose some of past information, especially in long sequences. In addition, many existing approaches just use the activity name of each event as the representative of that event. In this context, they may ignore other events’ attributes in generating the feature vector. Some works have utilized these attributes simply by concatenating all of them together. While we need to use all event attributes to predict the next activity, it should be noted that not all of them are equally important. In this paper, we use two layers of attention mechanism on top of LSTM: (i) at the attribute level, to determine which attributes have more importance; and (ii) at the event level, to identify important events in predicting the next activity. Experimental evaluation of the real-world event logs showed that the use of hierarchical attention mechanisms in the proposed approach could effectively predict the next activity of an ongoing process.