Process mining is a Business Process Management (BPM) technique that uses execution data of
business processes for their analysis. By transforming the data to so-called event logs
process mining tools generate process models that describe the executions as close as possible.
Process discovery can result either in graph-based notations (e.g. Petri nets or BPMN) or
declarative ones like Declare. One hypothesis in this work is that declarative constraint
templates can support model understanding in case process mining results in large confusing
spaghetti diagrams. Overall this work contributes an approach including a prototypical
implementation for applying association rule and sequential pattern mining to event logs for
discovering declarative process models. Preprocessing steps and the transformation of rules and
patterns to constraints in Declare are addressed explicitly. In this way analysts receive
transparent insights into the basis of the overall declarative model.
