Conformance checking of earthquake emergency processes based on activity-order decision trees

被引：0
作者：
Yang, Lifei [1 ]
Tian, Yinhua [1 ]
Liu, Zihao [1 ]
Han, Dong [2 ]
Du, Yuyue [3 ]
机构：
[1] College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an
[2] College of Continuing Education, Shandong University of Science and Technology, Tai'an
[3] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 08期
基金：
中国国家自然科学基金;
关键词：
conformance checking; decision tree; earthquake emergency processes; event logs; pruning;
D O I：
10.13196/j.cims.2023.BPM28
中图分类号：
学科分类号：
摘要：
To improve the low efficiency of the existing methods when judging whether traces arc compliant or not, a method to achieve business process conformance checking was proposed based on an activity-order decision trees, and then the tree was pruned to improve detection efficiency. The fit traces were visited in the event log, and the attribute values of activities were recorded according to the order of activities, then a decision tree was constructed taking the activity order as the attribute. With the guarantee of the precision, the decision tree was pruned to reduce its structural scale and improve the judgement efficiency, and the fitness of the traces was judged in the test set. Simulation experiments were implemented with the earthquake emergency plan process and the real life cases, which verified that the algorithm significantly had improved the business process conformance checking efficiency compared with the string distance metric based method and the classical alignment method. Based on the experimental results of real event cases, the feasibility and superiority of the proposed method in business process conformance checking were further illustrated. © 2024 CIMS. All rights reserved.
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收藏
页码：2872 / 2883
页数：11
相关论文
共 38 条
  • [1] SARKER I H., Machine learning: Algorithms, real-world applications and research directions [J ], SN Computer Science, 2, 3, pp. 1-21, (2021)
  • [2] LI Guojie, Further understanding of hig data [ J ], Big Data Research, 1, 1, pp. 8-16, (2015)
  • [3] HARIRI R H, FREDERICKS E M, BOWERS K M., Uncertainty in hig data analytics: Survey, opportunities, and challenges [ J ], Journal of Big Data, 6, 1, pp. 1-16, (2019)
  • [4] RAMASAMY A, CHOWDHURY S, Big data quality dimensions: A systematic literature review, Journal of Information Systems and Technology Management, 17, pp. 1-13, (2020)
  • [5] MENG Xiaofeng, DU Zhijuan, Research on the hig fusion: Issues and challenges[J], Journal of Computer Research and Development, 5 3, 2, pp. 231-246, (2016)
  • [6] PATI R, PUJARI A K, GAHAN P, Et al., Independent component analysis: A review with emphasis on commonly used algorithms and contrast function, Computacion y Sistemas, 25, 1, pp. 97-115, (2021)
  • [7] AUGUSTO A, MENDLING J, VIDGOF M, Et al., The connection hetween process complexity of event sequences and models discovered hy process mining, Information Sciences, 598, pp. 196-215, (2022)
  • [8] SANTOS GARCIA C, MEINCHEIM A, JUNIOR E R F, Et al., Process mining techniques and applications-A systematic mapping study, Expert Systems with Applications, 133, pp. 260-295, (2019)
  • [9] MARTIN N, FISCHERD A, KERPEDZHIEV G D, Et al., Opportunities and challenges for process mining in organizations: results of a delphi study, Business o- Information Systems Engineering, 63, 5, pp. 511-527, (2021)
  • [10] VAN DER AALST W M P, WEIJTERS T, MARUSRTER L., Workflow mining: discovering process models from event logs, IEEE Transactions on Knowledge and Data Engineering, 16, 9, pp. 1128-1142, (2004)
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