业务流程模型挖掘算法可靠性评价方法
DOI:
作者:
作者单位:

山东理工大学 计算机科学与技术学院

作者简介:

通讯作者:

中图分类号:

TP301

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)、科技部科技创新2030-“新一代人工智能”重大项目、山东省泰山学者特聘专家支持项目、山东省泰山学者工程专项基金资助项目、山东省自然科学基金优秀青年基金


Reliability Evaluation Method of a Business Process Model Mining Algorithm
Author:
Affiliation:

School of Computer Science and Technology, Shandong University of Technology

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    流程模型挖掘算法能够从事件日志中挖掘流程模型,不同流程模型挖掘算法处理事件日志的能力不同。目前,大量涉及流程模型挖掘算法评价的工作大都是间接评价,而间接评价存在局限性。针对这一问题,本文将可靠性(Reliability)作为模型挖掘算法的重要直接评价指标,提出一种模型挖掘算法可靠性评价的方法,旨在直接评价模型挖掘算法的性能。该方法对原始事件日志进行增量预处理以得到增量子日志集合;使用模型挖掘算法对增量子日志和原始事件日志进行处理,得到流程模型;最后,通过质量评估对业务流程模型挖掘算法的可靠性进行评价。基于公开的9个仿真事件日志和4个真实事件日志,从弱可靠性、噪声干扰可靠性和强可靠性三个方面对多个模型挖掘算法进行实验,实验结果表明:Heuristic Miner、Inductive Miner-infrequent、Inductive Miner和Alpha Miner可靠性值依次为4、3.2、2.4和1.6,可靠性值越高,算法的可靠性越强。可见本文方法能够有效地评价算法的可靠性。

    Abstract:

    Process model discovery algorithms are capable of extracting process models from event logs, but different algorithms have varying capabilities in handling event logs. Currently, most research on evaluating these algorithms involves indirect evaluation methods, which have limitations. To address this issue, a method is proposed to directly evaluate the reliability of process model discovery algorithms, using reliability as an important evaluation metric. The original event log is preprocessed to obtain an incremental sub-log collection, the process model discovery algorithm is applied to the incremental sub-logs and the original event log to obtain process models, and the reliability of the business process model discovery algorithm is evaluated through quality assessment. Based on nine public simulation event logs and four real event logs, multiple model discovery algorithms are experimented on from the aspects of weak reliability, noise interference reliability, and strong reliability. The experimental results show that the reliability values of Heuristic Miner, Inductive Miner-infrequent, Inductive Miner, and Alpha Miner are 4, 3.2, 2.4, and 1.6, respectively. Higher reliability values indicate stronger reliability of the algorithms. Thus, the proposed method can effectively evaluate the reliability of the algorithms.

    参考文献
    相似文献
    引证文献
引用本文

高庆鑫,刘聪. 业务流程模型挖掘算法可靠性评价方法[J]. 科学技术与工程, , ():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-04-12
  • 最后修改日期:2024-05-24
  • 录用日期:2024-06-05
  • 在线发布日期:
  • 出版日期:
×
亟待确认版面费归属稿件,敬请作者关注