  Long traces and large event logs that originate from sensors and prediction
models are becoming more common in our data-rich world. In such circumstances,
conformance checking, a key task in process mining, can become computationally
infeasible due to the exponential complexity of finding an optimal alignment.
  This paper introduces a novel sliding window approach to address these
scalability challenges while preserving the interpretability of alignment-based
methods. By breaking down traces into manageable subtraces and iteratively
aligning each with the process model, our method significantly reduces the
search space.
  The approach uses global information that captures structural properties of
the trace and the process model to make informed alignment decisions,
discarding unpromising alignments even if they are optimal for a local
subtrace. This improves the overall accuracy of the results.
  Experimental evaluations demonstrate that the proposed method consistently
finds optimal alignments in most cases and highlight its scalability. This is
further supported by a theoretical complexity analysis, which shows the reduced
growth of the search space compared to other common conformance checking
methods.
  This work provides a valuable contribution towards efficient conformance
checking for large-scale process mining applications.
