# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 1 Introduction  
To meet the increasing business demands, modern enterprises have raised the requirements for process modeling. The efficient and accurate establishment of business processes is a critical guarantee for BPM in modern enterprises and is one of the vital issues for business management software providers to accurately capture the flow of customers. However, process modeling is a time-consuming process that may require modeling a large combination of task nodes in the process, making it difficult for users to identify the appropriate node for forming a business diagram. Traditional human-based modeling approaches are time-consuming, imprecise, and require the modeler to have a high level of professionalism. Therefore, process modeling recommendation technology, which automatically recommends the next process node or the entire process based on the process nodes entered by the modeler, has been gaining more and more attention and research in the process management of enterprises.  
In the study of process modeling recommendations, one class of methods is based on log data [1,2]. However, due to the high requirements of the dataset and the limited application scope, process modeling recommendations based on the original process library datacan better satisfy most application scenarios. Currently, the relevant mainstream research is primarily divided into two categories. The process modeling recommendation based on graph structures [3; 4; 5; 6; 7; 8; 9] received more attention via related work, but it results in a loss of semantic information and a higher mining complexity. In contrast, the process recommendation method based on behavioral semantics [10; 11; 12; 13] simulates the execution behavior of the process through the process model structure and has a higher reference value.  
Therefore, this study proposed a process modeling recommendation method that combines disjoint paths and sequential patterns from the perspective of process behavior. It addresses the issues of poor accuracy and neglect of short-process models that are common in the two methods above. The main contributions of this study are as follows:  
1. A new edge-disjoint path extraction algorithm (EPE), which extracts the behavioral semantics of process library models based on the task-based process structure tree (TPST) [14], was proposed. This solves the problem of ignoring the semi-ordered structure characteristics of the TPST in the existing literature.
2. A contiguous path sequential pattern mining (CPSPan) algorithm for process recommendation was proposed; this algorithm mines frequent and continuous subsequences of edge-disjoint paths, increasing the number of process fragments to be matched in the reference model. This provides a greater range of options and more dependable guidance for process recommendations.
3. A new process recommendation strategy was proposed, in which the node recommendation degree was introduced to measure the influence of the node position on the recommendation. The lift, confidence, and support degrees were introduced as the basis for measuring the importance of the process fragments to be matched.
4. An experimental analysis demonstrated that the proposed method could effectively recommend the next node in the process modeling with an accuracy of 89.67% and 90.57% on the real and simulated datasets, respectively. Additionally, the method met the time requirements.