Event log sampling approach towards log completeness

被引：0
|
作者：
Su X. [1 ]
Liu C. [1 ]
Zhang S. [1 ]
Zeng Q. [2 ]
Li C. [1 ]
机构：
[1] School of Computer Science and Technology, Shandong University of Technology, Zibo
[2] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 10期
关键词：
event logs; log completeness; log sampling; model discovery; quality measure;
D O I：
10.13196/j.cims.2022.10.012
中图分类号：
学科分类号：
摘要：
The event log sampling method can improve the efficiency of model discovery. The existing sampling methods still have the problem of low efficiency and cannot guarantee the model quality when dealing with large-scale e-vent logs. Therefore，an event log sampling approach oriented log completeness was proposed，which included brute force sampling, set coverage sampling, trace length-based sampling and trace frequency-based sampling. The proposed sampling approaches had been implemented in the open-source process mining toolkit ProM. Furthermore, experiments using 9 public event log datasets from both time performance analysis and model quality evaluation showed that the proposed sampling approaches could greatly improve the efficiency of log sampling on the premise of ensuring the quality of model mining. © 2022 CIMS. All rights reserved.
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页码：3156 / 3165
页数：9
相关论文
共 23 条
  • [1] VAN DER AALST W M P., Data science in action, Process Mining, pp. 3-23, (2016)
  • [2] ZENG Q, SUN S X, DUAN H, Et al., Cross-organizational collaborative workflow mining from a multi-source log, Decision Support Systems, 54, 3, pp. 1280-1301, (2013)
  • [3] LIU C, DUAN H, ZENG Q, Et al., Towards comprehensive support for privacy preservation cross-organization business process mining [J], IEEE Transactions on Services Computing, 12, 4, pp. 639-653, (2019)
  • [4] VAN DER AALST W M P., Process mining: Discovery, conformance and enhancement of business processes, (2011)
  • [5] POURBAFRANI M, VAN ZELST S J, VAN DER AALST W M P., Scenario-based prediction of business processes using system dynamics [C], Proceedings of the OTM Confederated International Conferences, pp. 422-439, (2019)
  • [6] QAFARI M, VAN DER AALST W M P., Fairness-aware process mining, Proceedings of the OTM Confederated International Conferences, pp. 182-192, (2019)
  • [7] GAO J, VAN ZELST ST, LU X, Et al., Automated robotic process automation: A self-learning approach, Proceedings of the OTM Confederated International Conferences, pp. 95-112, (2019)
  • [8] VAN DER AALST W M P, WEIJTERS T, MARUSTER L., Workflow mining: Discovering process models from event logs [J], IEEE Transactions on Knowledge and Data Engineering, 16, 9, pp. 1128-1142, (2004)
  • [9] WEIJTERS A, RIBEIRO ITS., Flexible heuristics miner (FHM), Proceedings of 2011 IEEE Symposium on Com-putational Intelligence and Data Mining, pp. 310-317, (2011)
  • [10] LEEMANS S J J, FAHLAND D, VAN DER AALST W M P., Discovering block-structured process models from event logs-a constructive approach, Proceedings of International Conference on Applications and Theory of Petri nets and Concurrency, pp. 311-329, (2013)
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