  A business process model represents the expected behavior of a set of process
instances (cases). The process instances may be executed in parallel and may
affect each other through data or resources. In particular, changes in values
of data shared by process instances may affect a set of process instances and
require some operations in response. Such potential effects do not explicitly
appear in the process model. This paper addresses possible impacts that may be
affected through shared data across process instances and suggests how to
analyze them at design time (when the actual process instances do not yet
exist). The suggested method uses both a process model and a (relational) data
model in order to identify potential inter-instance data impact sets. These
sets may guide process users in tracking the impacts of data changes and
supporting their handling at runtime. They can also assist process designers in
exploring possible constraints over data. The applicability of the method was
evaluated using three different realistic processes. Using a process expert, we
further assessed the usefulness of the method, revealing some useful insights
for coping with unexpected data-related changes suggested by our approach.
