PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes


  • Veluru Chinnaiah Department of CSE, Vijaya Engineering College,Khammam,Telangana, India
  • Vadlamani Veerabhadram Department of C. S. E CVR College of Engineering, Hyderabad, Telangana, India
  • Ravi Aavula Department of CSE - DS, Anurag University, Hyderabad
  • Srinivas Aluvala Department of Computer Science and Artificial Intelligence, SR University



Process Mining, Artificial Intelligence, Deep Autoencoder, Long Short Term Memory, Deep Learning


We proposed a deep learning-based process mining framework known as PMiner for automatic detection of anomalies in business processes. Since there are thousands of business processes in real-time applications such as e-commerce, in the presence of concurrency, they are prone to exhibit anomalies. Such anomalies if not detected and rectified, cause severe damage to businesses in the long run. Our Artificial Intelligence (AI) enabled framework PMiner takes business process event longs as input and detects anomalies using a deep autoencoder. The framework exploits a deep autoencoder technique which is well-known for Its ability to discriminate anomalies. We proposed an algorithm known as Intelligent Business Process Anomaly Detector (IBPAD) to realize the framework. This algorithm learns from historical data and performs encoding and decoding procedures to detect business process anomalies automatically. Our empirical results using the BPI Challenge dataset, released by the IEEE Task Force on Process Mining, revealed that PMiner outperforms state-of-the-art methods in detecting business process anomalies. This framework helps businesses to identify process anomalies and rectify them in time to leverage business continuity prospects.




How to Cite

V. Chinnaiah, V. Veerabhadram, R. Aavula, and S. Aluvala, “PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes”, IJECES, vol. 15, no. 6, pp. 531-542, Jun. 2024.



Original Scientific Papers