  The importance of quality measures in process mining has increased. One of
the key quality aspects, generalization, is concerned with measuring the degree
of overfitting of a process model w.r.t. an event log, since the recorded
behavior is just an example of the true behavior of the underlying business
process. Existing generalization measures exhibit several shortcomings that
severely hinder their applicability in practice. For example, they assume the
event log fully fits the discovered process model, and cannot deal with large
real-life event logs and complex process models. More significantly, current
measures neglect generalizations for clear patterns that demand a certain
construct in the model. For example, a repeating sequence in an event log
should be generalized with a loop structure in the model. We address these
shortcomings by proposing a framework of measures that generalize a set of
patterns discovered from an event log with representative traces and check the
corresponding control-flow structures in the process model via their trace
alignment. We instantiate the framework with a generalization measure that uses
tandem repeats to identify repetitive patterns that are compared to the loop
structures and a concurrency oracle to identify concurrent patterns that are
compared to the parallel structures of the process model. In an extensive
qualitative and quantitative evaluation using 74 log-model pairs using against
two baseline generalization measures, we show that the proposed generalization
measure consistently ranks process models that fulfil the observed patterns
with generalizing control-flow structures higher than those which do not, while
the baseline measures disregard those patterns. Further, we show that our
measure can be efficiently computed for datasets two orders of magnitude larger
than the largest dataset the baseline generalization measures can handle.
