According to the Business Rules Group, "a business rule is a statement that defines or constrains some aspect of the business. It is intended to assert business structure or to control or influence the behavior of the business". [sic] Therefore business rules [BR] comprise the set of corporate policies, laws and industry standards, government regulations that are needed in order to properly run a business. The business rules can be enforced by people or software. One of the main characteristics of business rules is their propensity for frequent change, due to either internal or external factors to an enterprise. As these rules change frequently, immediate dissemination of these changes across people and sytems in an enterprise becomes vital. The delay in dissemination can adversely impact the reputation of the enterprise, and cause significant loss of revenue. To address these challenges, companies may choose to deploy business rules management systems [BRMS], which enable the rules to be captured in a central repository and uniformly applied across different applications. The current BRMS are often maintained by the IT group within a company, therefore the modifications of the BRs intended by the executive management would not be instantaneous, since they have to be coded, and tested before being deployed. Moreover, the executives might not have the possibility to make the best decisions, without having the benefit of analyzing historical data, and the possibility of quickly simulating what-if scenarios to visualize the effects of a possible rule change.

Some of the systems that provide this functionality are prohibitively expensive. This thesis proposes to solve those challenges by using the power of Big Data analysis to source, clean and analyze historical data that is used for mining business rule's [sic] conditions, matching rules, or creating new rules that can be visualized and immediately deployed without the intervention of the IT group. The solution proposed is based on open source tools, making it inexpensive, and highly maintainable. The implementation is web browser driven, therefore it can be operated from anywhere, anytime, on any device. Pure cloud implementation is also possible. The domain of application for this proof of concept application is the stop-loss rules used in financial systems.