Process mining serves a bridge between data mining
and business process modeling. The goal is to extract processrelated
knowledge from event data stored in information systems.
One of the most challenging process mining tasks is process
discovery, i.e., the automatic construction of process models from
raw event logs. Today there are dozens of process discovery
techniques generating process models using different notations
(Petri nets, EPCs, BPMN, heuristic nets, etc.). This paper focuses
on the representational bias used by these techniques. We will
show that the choice of target model is very important for the
discovery process itself. The representational bias should not
be driven by the desired graphical representation but by the
characteristics of the underlying processes and process discovery
techniques. Therefore, we analyze the role of the representational
bias in process mining.