Similarity Measure for String Sequences in Weighted Petri Net

Due to the fact that most existing process similarity metrics focus on a single dimension of the process and lack comprehensive consideration of process information, the accuracy of process retrieval needs to be improved and the application focuses on a single scenario. In this paper, an efficient and multidimensional similarity measure of string sequences of weighted Petri nets is proposed based on the structural and behavioral information. First, the proposed method weights the event log information to Petri nets. Then, the proposed method converts the weighted Petri net model into a string sequence using breadth-first traversal, and further divides the sequence into a set of immediately adjacent variation pairs with weights and a structural sequence and calculates the similarity value separately. Finally, the similarity value between processes is obtained by weighting. The experimental results show that the metric has a high accuracy rate of metric is 99.51% with a low time complexity.