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Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets

Author

Listed:
  • Cong Liu
  • Huiling Li
  • Qingtian Zeng
  • Ting Lu
  • Caihong Li

Abstract

To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

Suggested Citation

  • Cong Liu & Huiling Li & Qingtian Zeng & Ting Lu & Caihong Li, 2020. "Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-12, November.
  • Handle: RePEc:hin:jnlmpe:8836007
    DOI: 10.1155/2020/8836007
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