北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (5): 895-906.doi: 10.13700/j.bh.1001-5965.2017.0320

• 论文 • 上一篇    下一篇

基于统计α算法的过程挖掘

余建波1, 董晨阳1, 李传锋1, 程辉2, 孙习武2   

  1. 1. 同济大学 机械与能源工程学院, 上海 201804;
    2. 上海航天设备制造总厂, 上海 200245
  • 收稿日期:2017-05-15 出版日期:2018-05-20 发布日期:2018-05-29
  • 通讯作者: 余建波 E-mail:jbyu@tongji.edu.cn
  • 作者简介:余建波,男,博士,教授,博士生导师。主要研究方向:工作流管理、信号处理、质量控制等;董晨阳,男,硕士研究生。主要研究方向:工作流管理、过程挖掘等;李传锋,男,硕士研究生。主要研究方向:工作流管理、过程挖掘等。
  • 基金资助:
    国家自然科学基金(51375290,71777173);上海市航天科技创新基金(SAST2015054);中央高校基本科研业务费专项资金(22120180068)

Process mining based on statistical α-algorithm

YU Jianbo1, DONG Chenyang1, LI Chuanfeng1, CHENG Hui2, SUN Xiwu2   

  1. 1. School of Mechanical Engineering, Tongji University, Shanghai 201804, China;
    2. Shanghai Aerospace Equipment Manufacturing Factory, Shanghai 200245, China
  • Received:2017-05-15 Online:2018-05-20 Published:2018-05-29

摘要: 过程挖掘算法是从管理信息系统产生的事件日志中提取信息、发现知识并实现工作流建模的工具,也是目前工作流最主要的建模工具。然而现有的过程挖掘算法存在准确度较低、运行时间长和拟合度过高等问题,影响最终工作流模型的准确率。提出了一种基于统计α算法的过程挖掘算法,在保证算法较高的准确率和合适的拟合度的同时,降低算法运行时间,保证了算法的效率。首先,提出了重名活动识别算法,作为过程挖掘的预处理活动,提高了算法的准确性;其次,提出了统计α算法作为过程挖掘核心算法,有效消除了事件日志中噪声的影响;最后,提出了新的非自由选择结构识别算法,进一步提高了算法的鲁棒性和准确率。通过仿真实验和真实案例验证了该算法在准确率和运行时间上的优越性。

关键词: 工作流建模, 过程挖掘, 统计&alpha, 算法, 重名活动, 非自由选择结构

Abstract: Workflow technology is widely used in business process management. However, there are still many problems during the execution of business process because of the imperfect workflow model. Process mining is the most useful tool of workflow modeling, which can obtain objective and valuable information from event logs and build process model. Nevertheless, the existing process mining algorithms still have some problems, such as low accuracy, long operation time and overfitting, which will decreace the accuracy of the workflow model. This paper proposed a new process mining algorithm based on statistical α-algorithm, which can not only ensure the accuracy and suitable fitness, but also decrease the operation time. First, cognominal activity identification rules were proposed to be the pre-treated process of process mining, which could improve the accuracy of algorithm. Second, statistical α-algorithm was proposed as the core algorithm of process mining to eliminate the influence of noise in event logs. Moreover, a new algorithm was proposed to identify non-free-choice constructs, which improved the robustness and accuracy of the algorithm. The accuracy and efficiency of the algorithm are verified by simulation and real case.

Key words: workflow modeling, process mining, statistical α-algorithm, cognominal activities, non-free-choice constructs

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