  Process mining is a relatively new subject that builds a bridge between
traditional process modeling and data mining. Process discovery is one of the
most critical parts of process mining, which aims at discovering process models
automatically from event logs. The performance of existing process discovery
algorithms can be affected when there are missing activity labels in event
logs. Several methods have been proposed to repair missing activity labels, but
their accuracy can drop when a large number of activity labels are missing. In
this paper, we propose an LSTM-based prediction model to predict the missing
activity labels in event logs. The proposed model takes both the prefix and
suffix sequences of the events with missing activity labels as input.
Additional attributes of event logs are also utilized to improve the
performance. Our evaluation of several publicly available datasets shows that
the proposed method performed consistently better than existing methods in
terms of repairing missing activity labels in event logs.
