Abstract Existing work on workflflow mining ignores the dataflflow aspect of the problem. This is not acceptable for service-oriented applications that use Web services with typed inputs and outputs. We propose a novel algorithm WIT (Workflflow Inference from Traces) which identififies the context similarities of the observed actions based on the dataflflow and uses model merging techniques to generalize the control flflow and the dataflflow simultaneously. We identify the class of workflflows that WIT can learn correctly. We implemented WIT and tested it on a real world medical scheduling domain where WIT was able to fifind a good approximation of the target workflflow