  Modern information systems that support complex business processes generally
maintain significant amounts of process execution data, particularly records of
events corresponding to the execution of activities (event logs). In this
paper, we present an approach to analyze such event logs in order to
predictively monitor business goals during business process execution. At any
point during an execution of a process, the user can define business goals in
the form of linear temporal logic rules. When an activity is being executed,
the framework identifies input data values that are more (or less) likely to
lead to the achievement of each business goal. Unlike reactive compliance
monitoring approaches that detect violations only after they have occurred, our
predictive monitoring approach provides early advice so that users can steer
ongoing process executions towards the achievement of business goals. In other
words, violations are predicted (and potentially prevented) rather than merely
detected. The approach has been implemented in the ProM process mining toolset
and validated on a real-life log pertaining to the treatment of cancer patients
in a large hospital.
