[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.
刘彤,倪维健,孙宇健,曾庆田. 基于深度迁移学习的业务流程实例剩余执行时间预测方法*[J]. 数据分析与知识发现, 2020, 4(2/3): 134-142.
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.
( 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.)
Rogge-Solti A, Weske M . Prediction of Business Process Durations Using Non-Markovian Stochastic Petri Nets[J]. Information Systems, 2015,54:1-14.
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.
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.
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.
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.
Polato M, Sperduti A, Burattin A , et al. Time and Activity Sequence Prediction of Business Process Instances[J]. Computing, 2018,100(9):1005-1031.
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.
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.
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.
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.
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.
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.
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.
Radford A, Narasimhan K, Salimans T , et al. Improving Language Understanding with Unsupervised Learning[R]. OpenAI, 2018.
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.