Business Rules Management and Decision Mining - Filling in the Gaps

Proper decision-making is one of the most important capabilities of an organization. Adequately managing these decisions is therefore of high importance. Business Rules Management (BRM) is an approach that helps in managing decisions and underlying business logic. However, questions still arise if the decisions are properly improved based on decision data. Decision Mining (DM) could complement BRM capabilities in order to improve towards effective and efficient decision-making. In this study, we propose the integration of BRM and DM through a simulation using a government and a healthcare case. During this simulation, three entry points are presented that describe how decision-related data should be utilized between BRM capabilities and DM phases to be able to integrate them. The presented results provide a basis from which more technical research on the three DM phases can be further explored

Future Challenges in Decision Mining at Governmental Institutions

Decisions are made in fast-changing situations. To cope with this, decision mining could be utilized to support the decision-making process. Decision mining is an emerging field which could support an organizations decision-making process. For proper utilization of decision mining, possible challenges should be identified to take into account when mining decisions. As such, two focus groups were conducted where we identified 11 main challenges that seven Dutch governmental institutions deemed important and which should be taken into consideration when mining decisions. The identified challenges are depicted further together with existing literature and the coded observations. The identified challenges could be utilized as future research directions and are discussed as such

Utilizing Algorithms for Decision Mining Discovery

Organizations are executing operational decisions in fast changing environments, which increases the necessity for managing these decisions adequately. Information systems store information about such decisions in decision- and event logs that could be used for analyzing decisions. This study aims to find relevant algorithms that could be used to mine decisions from such decision- and event logs, which is called decision mining. By conducting a literature review, together with interviews conducted with experts with a scientific background as well as participants with a commercial background, relevant classifier algorithms and requirements for mining decisions are identified and mapped to find algorithms that could be used for the discovery of decisions. Five of the twelve algorithms identified have a lot of potential to use for decision mining, with small adaptations, while six out of the twelve do have potential but the required adaptation would demand too many alterations to their core design. One of the twelve was not suitable for the discovery of decisions