Redescription mining-based business process deviance analysisRedescription mining-based business process deviance analysisE. Ahmeti et al.

被引:0
|
作者
Engjëll Ahmeti [1 ]
Martin Käppel [2 ]
Stefan Jablonski [2 ]
机构
[1] Fraunhofer Institute for Manufacturing Engineering and Automation IPA Bayreuth,Institute for Computer Science
[2] University of Bayreuth,undefined
来源
Software and Systems Modeling | 2024年 / 23卷 / 6期
关键词
Deviance mining; Redescription mining; Process mining; Natural language generation;
D O I
10.1007/s10270-024-01231-8
中图分类号
学科分类号
摘要
Business processes often deviate from their expected or desired behavior. Such deviations can be either positive or negative, depending on whether or not they lead to better process performance. Deviance mining addresses the problem of identifying such deviations and explaining why a process deviates. In this paper, we propose a novel approach to identify and explain the causes of deviant process executions based on the technique of redescription mining, which extracts knowledge in the form of logical rules. By analyzing, comparing, and filtering these rules, the reasons for the deviant behaviors of a business process are identified both in general and for particular process instances. Afterward, the results of this analysis are transformed into a concise and well-readable natural language text that can be used by business analysts and process owners to optimize processes in a reasoned manner. We evaluate our approach from different angles using four process models and provide some advice for further optimization.
引用
收藏
页码:1421 / 1450
页数:29
相关论文
共 50 条
  • [1] Redescription mining-based business process deviance analysis
    Ahmeti, Engjell
    Kaeppel, Martin
    Jablonski, Stefan
    SOFTWARE AND SYSTEMS MODELING, 2024, 23 (06): : 1421 - 1450
  • [2] A Study of Process Mining-based Business Process Innovation
    Park, Sungbum
    Kang, Young Sik
    PROMOTING BUSINESS ANALYTICS AND QUANTITATIVE MANAGEMENT OF TECHNOLOGY: 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2016), 2016, 91 : 734 - 743
  • [3] A process mining-based analysis of business process work-arounds
    Outmazgin, Nesi
    Soffer, Pnina
    SOFTWARE AND SYSTEMS MODELING, 2016, 15 (02): : 309 - 323
  • [4] A process mining-based analysis of business process work-arounds
    Nesi Outmazgin
    Pnina Soffer
    Software & Systems Modeling, 2016, 15 : 309 - 323
  • [5] Mining Business Process Deviance: A Quest for Accuracy
    Hoang Nguyen
    Dumas, Marlon
    La Rosa, Marcello
    Maggi, Fabrizio Maria
    Suriadi, Suriadi
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2014 CONFERENCES, 2014, 8841 : 436 - 445
  • [6] Process mining-based business process management architecture: A case study in smart factories
    Olyai, A.
    Saraeian, S.
    Nodehi, A.
    SCIENTIA IRANICA, 2024, 31 (14) : 1122 - 1142
  • [7] Business Process Deviance Mining with Sequential and Declarative Patterns
    Di Francescomarino, Chiara
    Donadello, Ivan
    Ghidini, Chiara
    Maggi, Fabrizio Maria
    Puura, Joonas
    BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2025,
  • [8] Process mining-based medical program evolution
    Cao Yongzhong
    Zhu Junwu
    Guo Yalu
    Shi Chen
    COMPUTERS & ELECTRICAL ENGINEERING, 2018, 68 : 204 - 214
  • [9] A Process Mining-based approach for Attacker Profiling
    Rodriguez, Marcelo
    Betarte, Gustavo
    Calegari, Daniel
    2021 IEEE URUCON, 2021, : 425 - 429
  • [10] Frequent pattern mining-based log file partition for process mining
    Bantay, Laszlo
    Abonyi, Janos
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 123