A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns

Abstract

As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essential for effective BPM. However, the traditional manual-based modeling approach is time-consuming and error-prone. To overcome this, process recommendation technology can improve the intelligence and efficiency of modeling to a certain extent. However, existing process modeling recommendation methods suffer from the problem of low accuracy and neglecting short-process models. Therefore, a novel process modeling recommendation method that integrates disjoint paths and sequential patterns was proposed. This method uses edge-disjoint paths for the first time to represent the behavioral semantics of processes, and an improved contiguous sequential pattern mining algorithm was proposed to mine the contiguous path sequential patterns (CPSPs) of edge-disjoint paths. In the process modeling recommendation stage, the k CPSPs with the highest matching degree with the current reference model process were calculated, and the last node in these CPSPs was used as the set of recommendation nodes. In cases with CPSPs with the same matching degree, the one with the higher value was recommended according to their corresponding lift, confidence, and support degrees. Through experimental evaluation and comparison, it was shown that the proposed method effectively improved the accuracy of the recommendation of both short-process and long-process models while ensuring effectiveness and time efficiency.