Modeling the business process by mining multiple databases

Abstract

Institutional databases can be instrumental in understanding a business process, but additional databases are also needed to broaden the empirical perspective on the investigation. We present a few data mining principles by which a business process can be analyzed in quantitative details and new process components can be postulated. Sequential and parallel process decomposition can apply, guided by human understanding of the investigated process and the results of data mining. In a repeated cycle, human operators formulate open questions, use queries to get relevant data, use quests that 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 quantitative knowledge useful in understanding the university enrollment. Many discoveries have been made. The particularly surprising findings have been presented to the university administrators and affected the institutional policies.