  Conformance checking techniques allow us to quantify the correspondence of a
process's execution, captured in event data, w.r.t., a reference process model.
In this context, alignments have proven to be useful for calculating
conformance statistics. However, for extensive event data and complex process
models, the computation time of alignments is considerably high, hampering
their practical use. Simultaneously, it suffices to approximate either
alignments or their corresponding conformance value(s) for many applications.
Recent work has shown that using subsets of the process model behavior leads to
accurate conformance approximations. The accuracy of such an approximation
heavily depends on the selected subset of model behavior. Thus, in this paper,
we show that we can derive a priori error bounds for conformance checking
approximation based on arbitrary activity sequences, independently of the given
process model. Such error bounds subsequently let us select the most relevant
subset of process model behavior for the alignment approximation. Experiments
confirm that conformance approximation accuracy improves when using the
proposed error bound approximation to guide the selection of relevant subsets
of process model behavior.
