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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Process model forecasting method based on interactional evolution of directly-following relations

Zhang Runtao1
Fang Xianwen1,2
1. College of Mathematics & Big Data, Anhui University of Science & Technology, Huainan Anhui 232001, China
2. Anhui Province Engineering Laboratory for Big Data Analysis & Early Warning Technology of Cool Mine Safety, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0116

Abstract

Predictive Process Monitoring (PPM) serves as a key task in process mining, aiming to predict future process behavior based on the current event log. Most existing PPM approaches primarily perform short-term predictions for individual process instances, such as next activity prediction and remaining time estimation. These approaches offer limited prediction scopes and fail to provide a global perspective on process evolution, making it difficult to reveal long-term trends in process model changes. To address this limitation, a Process Model Forecasting (PMF) method based on time series analysis introduces a way to forecast the long-term evolution of process models. The method transforms raw event logs into multivariate time series, systematically capturing the temporal frequency evolution of all activity pairs (i. e. , directly-following relations) in the process. By modeling the mutual influences among direct successor relationships, the approach predicts the future direct follower graph, thereby enabling long-range forecasting of the entire process model. Experimental results demonstrate superior prediction accuracy and stability compared to traditional time series approaches across multiple real-life event logs. These results indicate strong potential for practical applications in monitoring and optimizing process behavior over time.

Foundation Support

国家自然科学基金资助项目(61572035)
安徽省重点研究与开发计划资助项目(2022a05020005)
安徽省自然科学基金资助项目(水科学联合基金)(2308085US11)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0116
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-17] Accepted Paper

Cite This Article

张润涛, 方贤文. 面向直接后继关系交互演化的过程模型预测方法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0116. (Zhang Runtao, Fang Xianwen. Process model forecasting method based on interactional evolution of directly-following relations [J]. Application Research of Computers, 2025, 42 (11). (2025-07-24). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0116. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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