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| Predicting Remaining Business Time with Deep Transfer Learning |
Liu Tong,Ni Weijian( ),Sun Yujian,Zeng Qingtian |
| College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China |
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Abstract [Objective] The paper tries to predict the remaining execution time of ongoing business process, aiming to provide better decision making support for process optimization.[Methods] We proposed a transfer learning framework for remaining time prediction, which constructed the prediction model with multi-layers recurrent neural networks. Then, we used representation learning method for events to pre-train the prediction model.[Results] We examined our model with five publicly available datasets and found the proposed approach outperforms the existing ones by 11% on average.[Limitations] The proposed model is of low interpretability, which limits its applications for real business management cases.[Conclusions] The proposed approach could help us predict remaining task processing time.
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Received: 20 June 2019
Published: 26 April 2020
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Corresponding Authors:
Weijian Ni
E-mail: niweijian@gmail.com
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