[2] van der Aalst W M P,[M]. Process mining: discovery, conformance and enhancement of business processes(2014).

[3] Pravilovic S, Appice A, Malerba D. Process mining to forecast the future of running cases[M]. ∥Appice A, Ceci M, Loglisci C, et al. New frontiers in mining complex patterns. Lecture notes in computer science. Cham: Springer, 8399, 67-81(2014).

[4] Kang B, Kim D, Kang S H. Periodic performance prediction for real-time business process monitoring[J]. Industrial Management & Data Systems, 112, 4-23(2012).

[5] Rogge-Solti A, Weske M. Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays[M]. ∥Basu S, Pautasso C, Zhang L, et al. Service-oriented computing. Lecture notes in computer science. Heidelberg: Springer, 8274, 389-403(2013).

[6] Zheng T T, Chen J X, Xu Y et al. Approach for individual task completion time prediction in business processes[J]. Computer Integrated Manufacturing Systems, 25, 993-1000(2019).

[7] Maggi F M, di Francescomarino C, Dumas M et al. Predictive monitoring of business processes[M]. ∥Jarke M, Mylopoulos J, Quix C, et al. Advanced information systems engineering. Lecture notes in computer science. Cham: Springer, 8484, 457-472(2014).

[8] Schonenberg M H, Song M. Time prediction based on process mining[J]. Information Systems, 36, 450-475(2011). http://dl.acm.org/citation.cfm?id=1891234

[9] Breuker D, Group H, Matzner M et al. Comprehensible predictive models for business processes[J]. MIS Quarterly, 40, 1009-1034(2016).

[10] Lakshmanan G T, Shamsi D, Doganata Y N et al. AMarkov prediction model for data-driven semi-structured business processes[J]. Knowledge and Information Systems, 42, 97-126(2015). http://link.springer.com/article/10.1007/s10115-013-0697-8

[11] Conforti R, de Leoni M, La Rosa M et al. Supporting risk-informed decisions during business process execution[M]. ∥Salinesi C, Norrie M C, Pastor O. Advanced information systems engineering. Lecture notes in computer science. Cham: Springer, 7908, 116-132(2013).

[12] Leontjeva A. Conforti R, di Francescomarino C, et al. Complex symbolic sequence encodings for predictive monitoring of business processes[M]. ∥Motahari-Nezhad H R, Recker J, Weidlich M. Business process management. Lecture notes in computer science. Cham: Springer, 9253, 297-313(2015).

[13] Evermann J, Rehse J R, Fettke P. A deep learning approach for predicting process behaviour at runtime[M]. ∥Dumas M, Fantinato M. Business process management workshops. Lecture notes in business information processing. Cham: Springer, 281, 327-338(2017).

[14] Greff K, Srivastava R K, Koutnik J et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 28, 2222-2232(2017).

[15] Zhu M K, Lu X L. Human action recognition algorithm based on Bi-LSTM-Attention model[J]. Laser & Optoelectronics Progress, 56, 151503(2019).

[16] Tax N, Verenich I, La Rosa M et al. Predictive business process monitoring with LSTM neural networks[M]. ∥Dubois E, Pohl K. Advanced information systems engineering. Lecture notes in computer science. Cham: Springer, 10253, 477-492(2017).

[17] Navarin N, Vincenzi B, Polato M et al. LSTM networks for data-aware remaining time prediction of business process instances[C]∥2017 IEEE Symposium Series on Computational Intelligence (SSCI), November 27-December 1, 2017, Honolulu, HI, USA, 477-492(2017).

[18] Tello-Leal E, Roa J, Rubiolo M et al. Predicting activities in business processes with LSTM recurrent neural networks[C]∥(2018).

[20] Yang H X, Chen F, Gan W F. Deep face recognition algorithm based on multitask learning[J]. Laser & Optoelectronics Progress, 56, 181005(2019).

[21] Hou C P, Jiang T L, Lang Y et al. Human activity andidentity multi-task recognition based on convolutional neural network using Doppler radar[J]. Laser & Optoelectronics Progress, 57, 021009(2020).

[22] [22] ZhengG, MukherjeeS, Dong XL, et al.OpenTag: open attribute value extraction from product profiles[C]∥Proceeding of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 19-23, 2018, London, England. New York: ACM Press, 2018: 1049- 1058.