Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be dificult, costly, and error prone. This presents a practical barrier to the adoption of process technologies. The barrier would be lowered by automatmg the creation of formal models. We are currently exploring techniques that can use basic event data captured from an on-going process to generate a formal model of process behavior. We term this kind of data analysis process discovery. Thts paper descrbes and illustrates three methods with whzch we have been experimenting: algorithmic grammar inference, Markov models, and neural networks.
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