  The aim of a process discovery algorithm is to construct from event data a
process model that describes the underlying, real-world process well.
Intuitively, the better the quality of the event data, the better the quality
of the model that is discovered. However, existing process discovery algorithms
do not guarantee this relationship. We demonstrate this by using a range of
quality measures for both event data and discovered process models. This paper
is a call to the community of IS engineers to complement their process
discovery algorithms with properties that relate qualities of their inputs to
those of their outputs. To this end, we distinguish four incremental stages for
the development of such algorithms, along with concrete guidelines for the
formulation of relevant properties and experimental validation. We will also
use these stages to reflect on the state of the art, which shows the need to
move forward in our thinking about algorithmic process discovery.
