The increasing availability of digital data offers new opportunities for analyzing business processes. Process aware information systems like Enterprise Resource Planning systems store data in the course of transaction processing. This data can be exploited by using process mining techniques. Process mining algorithms produce process models by analyzing recorded event logs. A fundamental challenge in process mining is the creation of purpose-oriented and useful process models. Process mining algorithms commonly refer to the temporal ordering of events for determining the control flow in reconstructed process models. We show how the logical sequence of events can be used instead of the temporal for reconstructing the control flow in mined process models. The exploitation of the logical structure of available event log records opens up new ways to receive purpose-oriented, less complex, and more informative process models.