  In process mining, precision measures are used to quantify how much a process
model overapproximates the behavior seen in an event log. Although several
measures have been proposed throughout the years, no research has been done to
validate whether these measures achieve the intended aim of quantifying
over-approximation in a consistent way for all models and logs. This paper
fills this gap by postulating a number of axioms for quantifying precision
consistently for any log and any model. Further, we show through
counter-examples that none of the existing measures consistently quantifies
precision.
