An eye into the future: leveraging a-priori knowledge in predictive business process monitoring

Predictive business process monitoring aims at leveraging
past process execution data to predict how ongoing (uncompleted)
process executions will unfold up to their completion. Nevertheless, cases
exist in which, together with past execution data, some additional knowledge (a-priori knowledge) about how a process execution will develop in
the future is available. This knowledge about the future can be leveraged
for improving the quality of the predictions of events that are currently
unknown. In this paper, we present two techniques - based on Recurrent
Neural Networks with Long Short-Term Memory (LSTM) cells - able to
leverage knowledge about the structure of the process execution traces
as well as a-priori knowledge about how they will unfold in the future
for predicting the sequence of future activities of ongoing process executions. The results obtained by applying these techniques on six real-life
logs show an improvement in terms of accuracy over a plain LSTM-based
baseline.