加权Petri网的字符串序列相似性度量

胡迎城; 邢玛丽; 吴元清

doi:10.12052/gdutxb.220178

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

加权Petri网的字符串序列相似性度量

综合研究 | 更新时间：2026-05-27

- 加权Petri网的字符串序列相似性度量
- Similarity Measure for String Sequences in Weighted Petri Net
- 广东工业大学学报 2024年41卷第1期页码：110-118
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  胡迎城 (1998–)，男，硕士研究生，主要研究方向为流程的相似性度量
  邢玛丽 (1990–) ，女，副教授，主要研究方向为流程数据管理、流程挖掘等，E-mail：maryxing@gdut.edu.cn
- 基金信息：
  
  国际重点研发计划项目 (2019YFB1705904)
- DOI：10.12052/gdutxb.220178
  中图分类号： TP301
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

KOPP A M, ORLOVSKYI D L. An approach to measure similarity of business process models[D]. Kharkiv Ukraine: National Technical University, 2018: 198-199.

YIN M, WEN L J, WANG J M, et al . Process similarity algorithm based on importance of transient adjacent relationships [J ] . Computer Integrated Manufacturing System, 2015, 22(2): 344-358.

CAHYAPRATAMA A, SARNO R. Gap analysis of business processes using behavioral, structural, and semantic similarity calculations [C]// 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT) . Makassar, Indonesia: IEEE, 2018: 192-196.

GUO W, ZENG Q, DUAN H, et al . Process-extraction-based text similarity measure for emergency response plans [J ] . Expert Systems with Applications, 2021(1): 115301.

TORKANFAR N, AZAR E R. Quantitative similarity assessment of construction projects using WBS-based metrics [J]. Advanced Engineering Informatics, 2020, 46: 101179.

LI D Y, FANG H. An approach of process similarity measurement based on activity occurrence relationship [J]. Control Theory and Application, 2020, 37(9): 2011-2019.

DONG Z H, WEN L J, HUANG H W, et al . Behavioral similarity algorithm for process models based on firing sequence collection [J ] . Journal of Software, 2015, 26(3): 449-459.

DUAN R, FANG H, FANG X W, et al . Process similarity algorithm based on editing distance of transition graph [J ] . Computer Application Research, 2020, 37(4): 1049-1053.

WU Y F, TAN W A. Method for calculating distance between business processes based on adjacency matrix [J]. Computer Engineering, 2018, 44(4): 52-58.

SONG J F, WEN L J, WANG J M. A similarity measure for process models based on task occurrence relations [J]. Computer Research and Development, 2017, 54(4): 832-843.

ZHOU C H, ZENG Q T, LIU C, et al . Business process similarity computing method based on process model structure and log behavior [J ] . Computer Integrated Manufacturing Systems, 2018, 24(7): 1793-1805.

LIU C, ZENG Q, CHENG L, et al . Measuring similarity for data-aware business processes [J ] . IEEE Transactions on Automation Science and Engineering, 2021, 19(2): 1070-1082.

ABID S, AMMAR H, MOBASHAR R, et al . An intelligent graph edit distance-based approach for finding business process similarities [J ] . Computers, Materials Continua, 2021(12): 3603-3618.

CAI Q M, ZHANG L, XU C H. Research of process similarity based on single-layer neural network [J]. Computer Engineering and Applications, 2022, 58(7): 295-302.

CHEN X SHAN M. A recommendation method for process modeling based on clustering and graph neural network[C]//2021 8th International Conference on Dependable Systems and Their Applications (DSA) . Yinchuan, China: IEEE, 2021: 195-201.

TRABELSI F Z, KHTIRA A, ASRI B E. Towards an approach of recommendation in business processes using decision trees[C]//2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC) . Rome, Italy: IEEE, 2021: 341-347.

WU Y F, TAN W A. Method for calculating the distance between process models based on the correlation matrix of Petri net [J]. Computer and Digital Engineering, 2018, 46(3): 429-436.

崔亮. 基于机器学习的业务流程系统的预测[D]. 北京: 北京邮电大学, 2019.

TAN W, XIE N, ZHAO L, et al . A new method for business process retrieval using breadth-first traversal [J ] . Enterprise Information Systems, 2020, 14(1): 83-109.

CAO B, WANG J X, AN W S, et al . ProBench: a benchmark dataset for evaluating the process similarity search methods [J ] . Computer Integrated Manufacturing Systems, 2017, 23(5): 1069-1079.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于概率标签Petri网的随机离散事件系统故障预测

基于Petri网可达性和结构的最大许可控制器设计

基于路况参数的混合动力机车能量管理策略研究

基于AAGV-YOLOX模型的输电线路异物检测研究

多套餐共享储能模式下考虑功率互济的社区联盟优化运行策略