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Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. So, contemporary workflow management systems are driven by explicit process models, i.e., completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as executed by the management. Therefore, this paper propose a workflow mining framework and architecture. Workflow mining is an approach to reverse the process and collect data at runtime to support workflow design and analysis. Note that in most cases, prior to the deployment of a workflow system, the workflow was already there. Also, note that in most information systems transactional data are registered (consider for example the transaction logs of ERP systems like SAP). The information collected at run-time can be used to derive a model explaining the events recorded. Such a model can be used in both the diagnosis phase and the (re)design phase. In contrast to a normal design which is often subjective and based on perceptions and opinions, mining is objective because it is based on the actual things that took place.
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