• DOI:10.48550/arXiv.2211.16687
• Corpus ID: 254096289

Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning

@article{Sim2022AutomaticDO,
  title={Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning},
  author={Sunghyun Sim and Ling Liu and Hyerim Bae},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.16687}
}

• Sunghyun Sim, Ling Liu, Hyerim Bae
• Published 30 November 2022
• Computer Science
• ArXiv

—Process mining is a methodology for derivation and analysis of processes models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has been made to incorporate and optimize multi-perspective process mining procedures. This paper contributes to developing solution approach to this problem. First, we propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning. Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps. Second, we propose a new method that further optimizes the experience replay (ER) method, one of the key algorithms of deep Q-learning, to improve the learning performance of reinforcement learning agents. Finally, we validate our approach using six real world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration. We show that our DERED approach can provide users with multi-perspective, high-quality process models that can be employed more conveniently for multi-perspective process mining.