Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 134-142    DOI: 10.11925/infotech.2096-3467.2019.0721
Current Issue | Archive | Adv Search |
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
Download: PDF(998 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      

[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.

URL:     OR

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
[1] van der Aalst W . Process Mining: Discovery, Conformance and Enhancement of Business Processes[M]. Springer, 2011.
[2] van der Aalst W, Schonenberg M H, Song M . Time Prediction Based on Process Mining[J]. Information Systems, 2011,36(2):450-475.
[3] 赵海燕, 李帅标, 陈庆奎 , 等. 面向业务过程的时间预测方法[J]. 小型微型计算机系统, 2019,40(2):280-286.
[3] ( Zhao Haiyan, Li Shuaibiao, Chen Qingkui , et al. Method of Time Prediction for Business Process[J]. Journal of Chinese Computer Systems, 2019,40(2):280-286.)
[4] Rogge-Solti A, Weske M . Prediction of Business Process Durations Using Non-Markovian Stochastic Petri Nets[J]. Information Systems, 2015,54:1-14.
[5] Verenich I, Nguyen H, La Rosa M , et al. White-box Prediction of Process Performance Indicators via Flow Analysis [C]//Proceedings of the 2017 International Conference on Software and System Process. ACM, 2017: 85-94.
[6] Tax N, Verenich I, La Rosa M , et al. Predictive Business Process Monitoring with LSTM Neural Networks [C]//Proceedings of the 29th International Conference on Advanced Information Systems Engineering. Springer, 2017: 477-492.
[7] Navarin N, Vincenzi B, Polato M , et al. LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances [C]//Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence. IEEE, 2017: 1-7.
[8] Verenich I, Dumas M, La Rosa M , et al. Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(4): Article No. 34.
[9] Polato M, Sperduti A, Burattin A , et al. Time and Activity Sequence Prediction of Business Process Instances[J]. Computing, 2018,100(9):1005-1031.
[10] Jimenez-Ramirez A, Barba I, Fernandez-Olivares J , et al. Time Prediction on Multi-Perspective Declarative Business Processes[J]. Knowledge and Information Systems, 2018,57(3):655-684.
[11] Senderovich A, Weidlich M, Gal A , et al. Queue Mining for Delay Prediction in Multi-Class Service Processes[J]. Information Systems, 2015,53:278-295.
[12] Bevacqua A, Carnuccio M, Folino F , et al. A Data-driven Prediction Framework for Analyzing and Monitoring Business Process Performances [C]//Proceedings of the 15th International Conference on Enterprise Information Systems. Springer, 2013: 100-117.
[13] Senderovich A, Di Francescomarino C, Ghidini C , et al. Intra and Inter-Case Features in Predictive Process Monitoring: A Tale of Two Dimensions [C]//Proceedings of the 15th International Conference on Business Process Management. Springer, 2017: 306-323.
[14] Leontjeva A, Conforti R, Di Francescomarino C , et al. Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes [C]//Proceedings of the 13th International Conference on Business Process Management. Springer, 2015: 297-313.
[15] Hochreiter S, Schmidhuber J . Long Short-Term Memory[J]. Neural Computation, 1997,9(8):1735-1780.
[16] Cho K, Van Merriënboer B, Bahdanau D , et al. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches[OL]. arXiv Preprint, arXiv:1409.1259.
[17] Chung J, Gulcehre C, Cho K H , et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[OL]. arXiv Preprint, arXiv:1412.3555.
[18] Radford A, Narasimhan K, Salimans T , et al. Improving Language Understanding with Unsupervised Learning[R]. OpenAI, 2018.
[19] Mikolov T, Sutskever I, Chen K , et al. Distributed Representations of Words and Phrases and Their Compositionality [C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 3111-3119.
[1] Xiang Fei,Xie Yaotan. Recognition Model of Patient Reviews Based on Mixed Sampling and Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 39-47.
[2] Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective[J]. 数据分析与知识发现, 2020, 4(1): 63-75.
[3] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[4] Jingjing Pei,Xiaoqiu Le. Identifying Coordinate Text Blocks in Discourses[J]. 数据分析与知识发现, 2019, 3(5): 51-56.
[5] Zhixiong Zhang,Huan Liu,Liangping Ding,Pengmin Wu,Gaihong Yu. Identifying Moves of Research Abstracts with Deep Learning Methods[J]. 数据分析与知识发现, 2019, 3(12): 1-9.
[6] Meishan Chen,Chenxi Xia. Identifying Entities of Online Questions from Cancer Patients Based on Transfer Learning[J]. 数据分析与知识发现, 2019, 3(12): 61-69.
[7] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[8] Changlei Fu,Li Qian,Huaping Zhang,Huaming Zhao,Jing Xie. Mining Innovative Topics Based on Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 46-54.
[9] Bengong Yu,Peihang Zhang,Qingtang Xu. Selecting Products Based on F-BiGRU Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(9): 22-30.
[10] Jiehua Wu,Jing Shen,Bei Zhou. Classifying Multilayer Social Network Links Based on Transfer Component Analysis[J]. 数据分析与知识发现, 2018, 2(9): 88-99.
[11] Wei Lu,Mengqi Luo,Heng Ding,Xin Li. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[12] Guoming Feng,Xiaodong Zhang,Suhui Liu. Classifying Chinese Texts with CapsNet[J]. 数据分析与知识发现, 2018, 2(12): 68-76.
[13] Yanhui Xiao,Xin Wang,Wen’gang Feng,Huawei Tian,Shaozhong Wu,Lihua Li. Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks[J]. 数据分析与知识发现, 2018, 2(10): 15-20.
[14] Wengang Feng,Jing Huang. Early Warning for Civil Aviation Security Checks Based on Deep Learning[J]. 数据分析与知识发现, 2018, 2(10): 46-53.
[15] Jiaheng Hu,Yonghua Cen,Chengyao Wu. Constructing Sentiment Dictionary with Deep Learning: Case Study of Financial Data[J]. 数据分析与知识发现, 2018, 2(10): 95-102.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938