Similarity Measure for String Sequences in Weighted Petri Net

Hu Ying-cheng; Xing Ma-li; Wu Yuan-qing

doi:10.12052/gdutxb.220178

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Similarity Measure for String Sequences in Weighted Petri Net

更新时间：2026-05-27

- Similarity Measure for String Sequences in Weighted Petri Net
- Vol. 41, Issue 1, Pages: 110-118(2024)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220178
  CLC： TP301
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
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胡迎城, 邢玛丽, 吴元清. 加权Petri网的字符串序列相似性度量[J]. 广东工业大学学报, 2024, 41(1): 110-118.

Hu Ying-cheng, Xing Ma-li, Wu Yuan-qing. Similarity Measure for String Sequences in Weighted Petri Net[J]. 2024, 41(1): 110-118.
胡迎城, 邢玛丽, 吴元清. 加权Petri网的字符串序列相似性度量[J]. 广东工业大学学报, 2024, 41(1): 110-118. DOI： 10.12052/gdutxb.220178.

Hu Ying-cheng, Xing Ma-li, Wu Yuan-qing. Similarity Measure for String Sequences in Weighted Petri Net[J]. 2024, 41(1): 110-118. DOI： 10.12052/gdutxb.220178.

摘要

由于现有的流程相似性度量方法大多只关注流程的单一维度，缺乏对流程信息的综合考虑，使得流程检索的准确率还有待提高。在综合考虑结构信息和行为信息下，提出了一种高效率、多维度的加权Petri网的字符串序列的相似性度量方法。该方法首先将事件日志信息加权至Petri网，然后使用广度优先遍历将加权Petri网模型转换为字符串序列，再将该序列分为一个带权重的紧邻变迁对集和一个结构序列并分别计算相似度值，最后加权得到流程之间的相似度值。实验结果表明，该度量方法准确率达到99.51%。另外，该方法在时间复杂度上也有着不错的优势。

Abstract

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.

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references

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