# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 3 Proposed Method  
This section proposes the specific process modeling recommendation method, the basic flow of which is shown in Figure 2.  
The method aims to assist modelers in rapid modeling by extracting the edge-disjoint paths of the process model and mining all the CPSPs. The process-matching degree between these sequential patterns and the edge-disjoint paths extracted from the reference model is computed. The top k sequential patterns in the process library with the highest process-matching degree with the reference model are selected, and the last node among them is used as the recommended node set.  
The overall framework of business process recommendation using the method in this study is shown in Figure 3, which is divided into three modules: preprocessing, CPSP mining, and node recommendation.  
1. The preprocessing module is divided into two parts. The first part analyzes the process model library in the TPST and saves it in the TPST library. Figure 4, for example, shows a TPST converted from the business process model in Figure 1. In the second part, all models in the TPST library are extracted according to the different gateway nodes and stored in the edge-disjoint path library.
2. The CPSP mining module mines CPSPin the edge-disjoint path library, and a sequential rule is generated according to the excavated CPSP and its corresponding calculated degree of lift, confidence, and support. All the sequential rules are saved in the sequential rule library for the subsequent recommendation.
3. The node recommendation module first calculates the process-matching degree between the edge-disjoint path and each CPSP in the sequential rule library excavated by the reference model. It then selects the appropriate recommendation result based on the process-matching degree, lift degree, confidence degree, and support degree. The specific recommended steps are as follows:  
Figure 2: The basic flow of the proposed method as represented using a UML activity diagram.  
1. The edge-disjoint path is extracted from the TPST that was transformed by the process model library in the preprocessing module using the EPE. 2. The frequent CPSP mining is carried out using CPSPan on the edge-disjoint path library. The corresponding degrees of lift, confidence, and support of each CPSP are calculated, and the results are saved into the sequential rule library T. 3. The corresponding TPST is generated by the process decomposition of the reference model, and its edge-disjoint paths are extracted. 4. All sequential rules in the sequential rule library are circled to calculate the process-matching degree between each sequential rule's CPSP and the edge-disjoint path in the reference model. 5. At the end of the cycle, the nodes are sorted according to the calculation result of the process-matching degree, and the recommended node set is the last node of the CPSP in the top k sequential rules after sorting. If there are CPSPs with the same matching degree, the nodes are ranked according to the degrees of lift, confidence, and support, and the larger value is recommended first.  
Figure 3: Business process recommendation framework.