Towards a Rule-Based Recommendation Approach for Business Process Modeling

Abstract

Business process modeling can be time-consuming and error-prone, especially for inexperienced users. For this reason, graphical editors for business process modeling should support users by providing suggestions on how to complete a currently developed business process model. We address this problem with a rule-based activity recommendation approach, which suggests suitable activities to extend the business process model that is currently edited at a user-defined position. Contrary to alternative approaches, rules provide an additional explanation for the recommendation, which can be useful in cases where a user might be torn between two alternatives. We plan to investigate how rule learning can be efficiently designed for the given problem setting and how a rule-based approach performs compared to alternative methods. In this paper we describe the basic idea, a first implementation and first results.