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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 134-142    DOI: 10.11925/infotech.2096-3467.2019.0721
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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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[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.

Key wordsRemaining Time Prediction      Business Process Instance      Deep Learning      Transfer Learning     
Received: 20 June 2019      Published: 26 April 2020
ZTFLH:  TP391  
Corresponding Authors: Weijian Ni     E-mail:

Cite this article:

Liu Tong,Ni Weijian,Sun Yujian,Zeng Qingtian. Predicting Remaining Business Time with Deep Transfer Learning. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 134-142.

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Framework of Remaining Time Prediction
Architecture of Bi-layer RNN
数据集 轨迹数量 事件数量 活动数量 轨迹最大长度 轨迹最小长度
BPIC2012_A 13 087 73 022 10 10 3
BPIC2012_O 5 015 41 728 7 39 4
BPIC2012_W 9 658 147 450 6 153 1
Helpdesk 3 804 13 710 9 14 1
Hospital_Billing 100 000 451 359 18 217 1
Statistics of Datasets
方法 BPIC2012_A BPIC2012_O BPIC2012_W Helpdesk Hospital_Billing
TS-set 7.505 8.429 7.392 6.283 51.456
TS-multiset 7.488 8.691 7.203 6.167 51.507
TS-sequence 7.488 8.619 9.612 6.192 51.504
SPN 8.880 8.516 6.385 6.337 78.018
LSTM 3.588 8.021 7.993 3.542 42.050
GRU 3.895 7.324 6.153 3.303 36.691
本文方法(LSTM) 3.489 5.858 5.826 3.357 33.201
本文方法(GRU) 3.512 7.306 6.338 2.677 32.227
Experiment Results
Results of Transfer Learning
Results of Pre-training
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