Abstract
Institutional databases can be instrumental in understanding a business process, but additional data may broaden the empirical perspective on the investigated process. We present a few data mining principles by which a business process can be analyzed and the results represented. Sequential and parallel process decomposition can apply in a data driven way, guided by a combination of automated discovery and human judgment. Repeatedly, human operators formulate open questions, use queries to prepare the data, issue quests to invoke automated search, and interpret the discovered knowledge. As an example we use mining for knowledge about student enrollment, which is an essential part of the university educational process. The target of discovery has been the understanding of the university enrollment. Many discoveries have been made. The particularly surprising findings have been presented to the university administrators and affected the institutional policies.
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Żytkow, J.M., Sanjeev, A.P. (1998). Business Process Understanding: Mining Many Datasets. In: Polkowski, L., Skowron, A. (eds) Rough Sets and Current Trends in Computing. RSCTC 1998. Lecture Notes in Computer Science(), vol 1424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-69115-4_33
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DOI: https://doi.org/10.1007/3-540-69115-4_33
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