  The field of predictive process monitoring focuses on case-level models to
predict a single specific outcome such as a particular objective, (remaining)
time, or next activity/remaining sequence. Recently, a longer-horizon,
model-wide approach has been proposed in the form of process model forecasting,
which predicts the future state of a whole process model through the
forecasting of all activity-to-activity relations at once using time series
forecasting.
  This paper introduces the concept of \emph{predictive process model
monitoring} which sits in the middle of both predictive process monitoring and
process model forecasting. Concretely, by modelling a process model as a set of
constraints being present between activities over time, we can capture more
detailed information between activities compared to process model forecasting,
while being compatible with typical predictive process monitoring objectives
which are often expressed in the same language as these constraints. To achieve
this, Processes-As-Movies (PAM) is introduced, i.e., a novel technique capable
of jointly mining and predicting declarative process constraints between
activities in various windows of a process' execution. PAM predicts what
declarative rules hold for a trace (objective-based), which also supports the
prediction of all constraints together as a process model (model-based).
Various recurrent neural network topologies inspired by video analysis tailored
to temporal high-dimensional input are used to model the process model
evolution with windows as time steps, including encoder-decoder long short-term
memory networks, and convolutional long short-term memory networks. Results
obtained over real-life event logs show that these topologies are effective in
terms of predictive accuracy and precision.
