Intelligent Process Mining Using Artificial Intelligence Techniques

  • Authors

    • Chinedu Eze Department of Electrical Engineering, Enugu Technical University, Nigeria Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2022PI6L1W

    Published 02-03-2022

  • Process Mining, Artificial Intelligence, Machine Learning, Deep Learning, Event Logs, Predictive Analytics, Business Process Management

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    C. Eze, “Intelligent Process Mining Using Artificial Intelligence Techniques”, IJAIDT, vol. 5, no. 1, pp. 01–15, Feb. 2022, doi: 10.67228/30713315/IJAIDT-2022PI6L1W.

  • Abstract

    Process mining bridges data science and business process management by analyzing event logs to understand process behavior, performance, and compliance. However, traditional methods struggle with complex, large, and dynamic data. This paper proposes an AI-based process mining system that uses machine learning, deep learning, and optimization techniques to improve process discovery, conformance checking, and predictive analytics. The system can handle noisy and incomplete data and uses models like decision trees, SVMs, clustering, and neural networks to uncover hidden patterns. It introduces an adaptive process discovery model that updates in real time and applies reinforcement learning to optimize decision-making and reduce bottlenecks. Experimental results show improved accuracy and efficiency compared to traditional approaches. The paper also highlights challenges such as scalability, interpretability, and data privacy, suggesting future work in explainable AI and blockchain for secure process tracking.

  • References

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