Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach

The rapid expansion of the Internet has resulted not only in the ever-growing amount of data stored therein, but also in the burgeoning complexity of the concepts and phenomena pertaining to that data. This issue has been vividly compared by the renowned statistician J.F. Friedman (Friedman, 1997) of Stanford University to the advances in human mobility from the period of walking afoot to the era of jet travel. These essential changes in data have brought about new challenges in the discovery of new data mining methods, especially the treatment of these data that increasingly involves complex processes that elude classic modeling paradigms. “Hot” datasets like biomedical, financial or net user behavior data are just a few examples. Mining such temporal or stream data is a focal point in the agenda of many research centers and companies worldwide (see, e.g., (Roddick et al., 2001; Aggarwal, 2007)). In the data mining community, there is a rapidly growing interest in developing methods for process mining, DOI: 10.4018/978-1-60566-324-1.ch002