# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 6 Discussion and Limitations  
BPM plays a crucial role in the digitalization and informatization processes of enterprises. The ultimate goal of BPM is to achieve the optimal realization of business processes. However, several factors can affect the performance of these processes. Previous research predominantly focused on optimizing business processes in terms of process efficiency, resource utilization, customer experience, and cost reduction, yielding significant results. Process recommendation technology can also be viewed as a means of optimizing the customer experience to a certain extent, involving the consideration of factors such as user modeling requirements, offline and online processing techniques, and time constraints. By analyzing the model structure of the business process and current business requirements, process recommendation technology can suggest the most suitable process execution path, thereby enhancing the efficiency and accuracy of the business process. Our contribution to the academic and practical community is a novel process recommendation technique that combines disjoint path and sequential patterns. This technique automatically recommends the next process node during the process modeling based on the current process node entered by the modeler. In order to address the issues of low accuracy and neglect of short-process models, which were the main focus of this study, we conducted experiments on both real and simulated datasets to evaluate the effectiveness of the proposed method. The \"Experimental Results and Analysis\" section demonstrates that our process recommendation technique achieved a higher hit rate and accuracy and met the time response requirements. Specifically, the comparison method MCS proposed in [4] uses graph structures for process recommendation, but the proposed graph-matching method can have a large variance in results due to overly complex graphs. The MDS proposed in [12] measures the similarity between processes by distance, but the significance of the \"distance\" value does not reflect the similarity between processes that contain a loop structure well. The TPS proposed in [25] requires the modeler to develop the target profile for each reference model being modeled, which has high professional requirements for the modeler and is challenging to execute. The VPS proposed in [26] uses the vertex-disjoint paths to express behavioral semantics, which cannot fully reflect the absence of order among the child nodes of the parallel and selective structure of the process. Moreover, this method ignores the fact that there may be multiple paths to complete the same business goal. Furthermore, [25; 26] ignored the existence of duplicate nodes in the process model. In summary, the method proposed in this study has the following advantages:  
1. This study proposed a method that focuses on the behavioral semantics of the process and uses edge-disjoint path parsing, which offers a more significant efficiency advantage and smaller result differences compared with graph parsing. 2. The proposed method uses the EPE to more accurately represent the behavioral semantics of parallel and selective structures. 3. The method also introduces a node recommendation degree to measure the importance of different node positions in the paths and selects the last node of the CPSP as the recommendation result, thus avoiding the influence of duplicate nodes on the result. 4. Unlike previous methods (such as [12] and [26]) that only consider the frequency of the node appearance in the process model library, this study introduced lift and confidence measures to evaluate nodes that are more closely related to the recommended fragment, thereby improving the accuracy. 5. CPSPan was utilized to perform further knowledge discovery of edge-disjoint paths and obtain a wider range of options and more reliable guidance for process recommendations. In addition, we developed a visual prototype system that includes features such as recommendation, addition, deletion, editing, and dragging of nodes.