Interactive Process Drift Detection Framework

被引:2
|
作者
Vecino Sato, Denise Maria [1 ,2 ]
Barddal, Jean Paul [1 ]
Scalabrin, Edson Emilio [1 ]
机构
[1] Pontifical Catholic Univ Parana PUCPR, Imac Conceicao 1155, BR-80215901 Curitiba, Parana, Brazil
[2] Fed Inst Parana IFPR, Joao Negrao 1285, BR-80230150 Curitiba, Parana, Brazil
关键词
Process drift; Concept drift; Drift detection; Evolving environment;
D O I
10.1007/978-3-030-87897-9_18
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel tool for detecting drifts in process models. The tool targets the challenge of defining the better parameter configuration for detecting drifts by providing an interactive user interface. Using this interface, the user can quickly change the parameters and verify how the process evolved. The process evolution is presented in a timeline of process models, simulating a "replay" of models over time. One instantiation of the framework was implemented using a fixed-size sliding window, discovering process maps using directly-follows graphs (DFGs), and calculating nodes and edges similarities. This instantiation was evaluated using a benchmarking dataset of simple and complex drift patterns. The tool correctly detected 17 from the 18 change patterns, thus confirming its potential when an adequate window size is set. The user interface shows that replaying the process models provides a visual understanding of the changing process. The concept drift is explained by the similarity metrics' differences, thus allowing drift localization.
引用
收藏
页码:192 / 204
页数:13
相关论文
共 50 条
  • [41] A fog computing based concept drift adaptive process mining framework for mobile APPs
    Huang, Tao
    Xu, Boyi
    Cai, Hongming
    Du, Jiawei
    Chao, Kuo-Ming
    Huang, Chengxi
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 89 : 670 - 684
  • [42] CONDA-PM-A Systematic Review and Framework for Concept Drift Analysis in Process Mining
    Elkhawaga, Ghada
    Abuelkheir, Mervat
    Barakat, Sherif I.
    Riad, Alaa M.
    Reichert, Manfred
    ALGORITHMS, 2020, 13 (07)
  • [43] Process with inert drift
    White, David
    ELECTRONIC JOURNAL OF PROBABILITY, 2007, 12 : 1509 - 1546
  • [44] Exploring Concept Drift using Interactive Simulations
    Smith, Jeremiah
    Dulay, Naranker
    Toth, Mate Attila
    Amft, Oliver
    Zhang, Yanxia
    2013 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS), 2013, : 49 - 54
  • [45] A framework for interactive proof
    Aspinall, David
    Lueth, Christoph
    Wintersteini, Daniel
    TOWARDS MECHANIZED MATHEMATICAL ASSISTANTS, 2007, 4573 : 161 - +
  • [46] Hierarchical Reduced-Space Drift Detection Framework for Multivariate Supervised Data Streams
    Zhang, Shuyi
    Tino, Peter
    Yao, Xin
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (03) : 2628 - 2640
  • [47] Semi-supervised concept drift detection and adaptation based on conformal martingale framework
    Zhang, Yu
    Zhou, Ping
    Zhang, Ruiyao
    Lu, Shaowen
    Chai, Tianyou
    JOURNAL OF PROCESS CONTROL, 2025, 147
  • [48] A novel framework for concept drift detection using autoencoders for classification problems in data streams
    Ali, Usman
    Mahmood, Tariq
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2025, 16 (01) : 397 - 418
  • [49] Implementation and Validation of Business Process Deviation Detection Framework
    Achmadi, Budi J.
    Hendradjaya, Bayu
    Sunindyo, Wikan D.
    2015 INTERNATIONAL CONFERENCE ON DATA AND SOFTWARE ENGINEERING (ICODSE), 2015, : 120 - 125
  • [50] Looking for Change: A Computer Vision Approach for Concept Drift Detection in Process Mining
    Kraus, Alexander
    van der Aa, Han
    BUSINESS PROCESS MANAGEMENT, BPM 2024, 2024, 14940 : 273 - 290