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Predictive Process Monitoring Based on Optimized Deep Learning Methods

Alibakhshi, Alireza | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57657 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Hassannayebi, Erfan
  7. Abstract:
  8. Business processes are an essential part of every business as they provide insights on how to optimize and make them more efficient. Predictive Business Process Monitoring has garnered significant attention in recent years due to its capability to forecast process outcomes and predict the next activity within an ongoing process. In the last few years, there have been works that focused on deep learning and its applications in predicting the next activity. Some research used Long Term Short Memory, while others used Convolutional Neural Networks. However, long term short term memory models have the constraint of relatively slow training, while Convolutional Neural Networks are fast but may reach lower accuracy. In previous studies, researchers attempted to use meta-attributes to improve the accuracy of next activity prediction. However, in this research, a network that includes a layer of Bi-Directional Long Term Short Memory and utilizes attention mechanisms is introduced to determine the dependencies and importance of activities. At the same time, this network benefits from the fast training properties of the Convolutional Neural Network. This research also notes that gathering meta-data is not always efficient for businesses and may be overlooked in real-life systems. Therefore, only activity and sequence are used as input and managed to surpass the state-of-the-art deep learning models in designated experiments. Additionally, a case study was conducted on Order to Cash to test Attention based Multi Channel Convolutional Neural Network in a real-life situation and achieved an accuracy of 87%
  9. Keywords:
  10. Deep Learning ; Statistical Machine Learning ; Process Mining ; Convolutional Neural Network ; Next Activity Prediction ; Predictive Business Process Monitoring

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