Aggregating causal runs into workflow nets

This paper provides three algorithms for constructing system nets from sets of partially-ordered causal runs. The three aggregation algorithms differ with respect to the assumptions about the information contained in the causal runs. Specifically, we look at the situations where labels of con- ditions (i.e. references to places) or events (i.e. references to transitions) are unknown. Since the paper focusses on aggregation in the context of process mining, we solely look at work ow nets, i.e. the class of Petri nets with unique start and end places. The dierence of the work presented here and most work on process mining is the assumption that events are logged as partial orders instead of linear traces. Although the work is inspired by applications in the process mining and work ow domains, the results are generic and can be applied in other application domains. Keywords: Process mining, Petri net Synthesis, Aggregation, Runs, Process nets