KSII Transactions on Internet and Information Systems (TIIS)

  • 제18권8호
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  • Pages.2316-2332
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  • 2024
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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A Web-based System for Business Process Discovery: Leveraging the SICN-Oriented Process Mining Algorithm with Django, Cytoscape, and Graphviz

  • Thanh-Hai Nguyen (Thai Nguyen University) ;
  • Kyoung-Sook Kim (Contents Convergence Software Research Institute, Kyonggi University) ;
  • Dinh-Lam Pham (Contents Convergence Software Research Institute, Kyonggi University) ;
  • Kwanghoon Pio Kim (Division of AI Computer Science and Engineering, Kyonggi University)
  • 투고 : 2024.01.04
  • 심사 : 2024.08.04
  • 발행 : 2024.08.31
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초록

In this paper, we introduce a web-based system that leverages the capabilities of the ρ(rho)-algorithm, which is a Structure Information Control Net (SICN)-oriented process mining algorithm, with open-source platforms, including Django, Graphviz, and Cytoscape, to facilitate the rediscovery and visualization of business process models. Our approach involves discovering SICN-oriented process models from process instances from the IEEE XESformatted process enactment event logs dataset. This discovering process is facilitated by the ρ-algorithm, and visualization output is transformed into either a JSON or DOT formatted file, catering to the compatibility requirements of Cytoscape or Graphviz, respectively. The proposed system utilizes the robust Django platform, which enables the creation of a userfriendly web interface. This interface offers a clear, concise, modern, and interactive visualization of the rediscovered business processes, fostering an intuitive exploration experience. The experiment conducted on our proposed web-based process discovery system demonstrates its ability and efficiency showing that the system is a valuable tool for discovering business process models from process event logs. Its development not only contributes to the advancement of process mining but also serves as an educational resource. Readers, students, and practitioners interested in process mining can leverage this system as a completely free process miner to gain hands-on experience in rediscovering and visualizing process models from event logs.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, Ministry of Science and ICT), Republic of Korea (Grant No. NRF-2022R1A2C2093002).

참고문헌

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