  Process mining enables the analysis of complex systems using event data
recorded during the execution of processes. Specifically, models of these
processes can be discovered from event logs, i.e., sequences of events.
However, the recorded events are often too fine-granular and result in
unstructured models that are not meaningful for analysis. Log abstraction
therefore aims to group together events to obtain a higher-level representation
of the event sequences. While such a transformation shall be driven by the
analysis goal, existing techniques force users to define how the abstraction is
done, rather than what the result shall be. In this paper, we propose GECCO, an
approach for log abstraction that enables users to impose requirements on the
resulting log in terms of constraints. GECCO then groups events so that the
constraints are satisfied and the distance to the original log is minimized.
Since exhaustive log abstraction suffers from an exponential runtime
complexity, GECCO also offers a heuristic approach guided by behavioral
dependencies found in the log. We show that the abstraction quality of GECCO is
superior to baseline solutions and demonstrate the relevance of considering
constraints during log abstraction in real-life settings.
