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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1762-1768

Method for business process outcome prediction based on behavior profile matrix enhancement

Liu Heng1
Fang Xianwen1,2
Lu Ke1,2
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 Coal Mine Safety, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0524

Abstract

Predictive process monitoring(PPM) relies on predictive effectiveness, and to address the challenge of improving predictive performance in PPM, this paper proposed a novel approach called behavior profile matrix enhanced business process outcome prediction. Initially, this approach extracted the behavior profile matrix by analyzing the interactions among activities and incorporated it into the model along with event sequences. Then, it used convolutional neural networks(CNN) and long short-term memory networks(LSTM) to independently capture image features from the matrix and sequence features. Finally, this approach integrated an attention mechanism to seamlessly combine both image and sequence features for predictive purposes. Validation using real event logs demonstrates that the proposed enhancement method significantly enhances predictive performance compared to the baseline LSTM prediction methods when forecasting event log outcomes, confirming the feasibility of this approach. This approach combines the behavior profile matrix to enhance the predictive model's understanding of relationships between behaviors in event logs, consequently leading to an improvement in predictive performance.

Foundation Support

国家自然科学基金资助项目(61572035,61402011)
安徽省重点研究与开发计划资助项目(2022a05020005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0524
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Algorithm Research & Explore
Pages: 1762-1768
Serial Number: 1001-3695(2024)06-023-1762-07

Publish History

[2024-01-12] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

刘恒, 方贤文, 卢可. 基于行为轮廓矩阵增强的业务流程结果预测方法 [J]. 计算机应用研究, 2024, 41 (6): 1762-1768. (Liu Heng, Fang Xianwen, Lu Ke. Method for business process outcome prediction based on behavior profile matrix enhancement [J]. Application Research of Computers, 2024, 41 (6): 1762-1768. )

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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