  The real-time prediction of business processes using historical event data is
an important capability of modern business process monitoring systems. Existing
process prediction methods are able to also exploit the data perspective of
recorded events, in addition to the control-flow perspective. However, while
well-structured numerical or categorical attributes are considered in many
prediction techniques, almost no technique is able to utilize text documents
written in natural language, which can hold information critical to the
prediction task. In this paper, we illustrate the design, implementation, and
evaluation of a novel text-aware process prediction model based on Long
Short-Term Memory (LSTM) neural networks and natural language models. The
proposed model can take categorical, numerical and textual attributes in event
data into account to predict the activity and timestamp of the next event, the
outcome, and the cycle time of a running process instance. Experiments show
that the text-aware model is able to outperform state-of-the-art process
prediction methods on simulated and real-world event logs containing textual
data.
