An efficient workcase classification method and tool in workflow mining

被引:0
作者
Park, MJ [1 ]
Kim, KH [1 ]
Kim, CM [1 ]
机构
[1] Kyonggi Univ, Dept Comp Sci, Collaborat Technol Res Lab, Suwon 442760, Kyonggido, South Korea
来源
Fourth Annual ACIS International Conference on Computer and Information Science, Proceedings | 2005年
关键词
workflow mining; workcase classification method and tool; reachable-path rediscovery; activity firing sequence;
D O I
暂无
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(1) conceives a workcase classification method and implements it as a tool so as to be used in workflow mining systems. The method is for resolving the workcase classification problem issued for mining an activity firing or execution sequence of a workcase from monitoring and audit logs. That is, it finally generates a workcase classification decision tree consisting of a minimal set of critical activities to be used for deciding the corresponding researchable-path of the workcases. Why is the method efficient? Because it uses a minimal decision tree in classifying workcases' reachable-paths. And the tool is a graphical visualizer of the method, and consists of three subsystems used to automatically generate information control net, activity dependency net and minimal activity net through their corresponding algorithms. Especially the method and tool might be an impeccable solution for the specific domain of massively parallel large-scale workflow procedures. In a consequence, workflow mining methodologies and systems are rapidly growing and coping with a wide diversity of domains in terms of their applications and working environments. So, the literature needs various, advanced, and specialized workflow mining techniques and architectures that are used for finally feed-backing their analysis results to the redesign and reengineering phase of the existing workflow and business process models. We strongly believe that this work might be one of those impeccable attempts and pioneering contributions for pioneering and advancing the workflow mining technology.
引用
收藏
页码:80 / 85
页数:6
相关论文
共 38 条
  • [1] Workflow mining with InWoLvE
    Herbst, J
    Karagiannis, D
    COMPUTERS IN INDUSTRY, 2004, 53 (03) : 245 - 264
  • [2] Scientific Workflow Mining in Clouds
    Song, Wei
    Chen, Fangfei
    Jacobsen, Hans-Arno
    Xia, Xiaoxu
    Ye, Chunyang
    Ma, Xiaoxing
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2017, 28 (10) : 2979 - 2992
  • [3] Current Trends in Workflow Mining
    Ajayi, L. K.
    Azeta, A. A.
    Owolabi, I. T.
    Damilola, O. O.
    Chidozie, F.
    Azeta, A. E.
    Amosu, O.
    3RD INTERNATIONAL CONFERENCE ON SCIENCE AND SUSTAINABLE DEVELOPMENT (ICSSD 2019): SCIENCE, TECHNOLOGY AND RESEARCH: KEYS TO SUSTAINABLE DEVELOPMENT, 2019, 1299
  • [4] A Comparative Study of Workflow Mining Systems
    Abdelkafi, Mahdi
    Bousabeh, Dorra
    Bouzguenda, Lotfi
    Gargouri, Faiez
    2012 6TH INTERNATIONAL CONFERENCE ON SCIENCES OF ELECTRONICS, TECHNOLOGIES OF INFORMATION AND TELECOMMUNICATIONS (SETIT), 2012, : 939 - 945
  • [5] Workflow mining for visualization and analysis of surgeries
    Tobias Blum
    Nicolas Padoy
    Hubertus Feußner
    Nassir Navab
    International Journal of Computer Assisted Radiology and Surgery, 2008, 3 : 379 - 386
  • [6] Towards mining structural workflow patterns
    Gaaloul, W
    Baïna, K
    Godart, C
    DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2005, 3588 : 24 - 33
  • [7] Workflow mining based on role modules
    Zhao, Weidong
    Ye, Mao
    PROCEEDINGS OF THE 2007 CONFERENCE ON SYSTEMS SCIENCE, MANAGEMENT SCIENCE AND SYSTEM DYNAMICS: SUSTAINABLE DEVELOPMENT AND COMPLEX SYSTEMS, VOLS 1-10, 2007, : 3055 - 3060
  • [8] Workflow mining: A survey of issues and approaches
    van der Aalst, WMP
    van Dongen, BF
    Herbst, J
    Maruster, L
    Schimm, G
    Weijters, AJMM
    DATA & KNOWLEDGE ENGINEERING, 2003, 47 (02) : 237 - 267
  • [9] Log sequence clustering for workflow mining in multi-workflow systems
    Liu, Xumin
    Alshangiti, Moayad
    Ding, Chen
    Yu, Qi
    DATA & KNOWLEDGE ENGINEERING, 2018, 117 : 1 - 17
  • [10] Workflow mining for visualization and analysis of surgeries
    Blum, Tobias
    Padoy, Nicolas
    Feussner, Hubertus
    Navab, Nassir
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2008, 3 (05) : 379 - 386