Prediction method of business process remaining time based on attention bidirectional recurrent neural network
Abstract
Most of the existing deep learning-based business process remaining time prediction methods use traditional long-short-term memory recurrent neural networks to build prediction models. Due to the limited modeling ability of traditional long-short-term memory recurrent neural networks for sequence data, and existing methods there is still much room for improvement in the prediction effect. Aiming at the shortcomings of existing methods, this paper proposes a business process remaining time prediction method based on attention bidirectional recurrent neural network. The method uses a bidirectional recurrent neural network to model the process instance data and introduces an attention mechanism to automatically learn the weights of different events in the process instance. In addition, in order to further improve the learning effect, an iterative learning strategy is designed based on the idea of transfer learning, which builds remaining time prediction models for process instances of different lengths, which improves the pertinence of the model. The experimental results show that the proposed method has obvious advantages compared with traditional methods.
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1. Teinemaa I, Dumas M, Rosa ML, et al. Outcomeoriented predictive process monitoring: Review and benchmark. ACM Transactions on Knowledge Discovery from Data (TKDD) 2019; 13(2): 1–57. doi: 10.1145/3301300.
2. der Aalst WMPV, Schonenberg MH, Song M. Time prediction based on process mining. Information Systems 2011; 36(2): 450–475. doi: 10.1016/j.is.2010.09.001.
3. Navarin N, Vincenzi B, Polato M, et al. LSTM networks for data-aware remaining time prediction of business process instances. In: Proceedings of 2017 IEEE Symposium Series on Computational Intelligence (SSCI); 2017 Nov 27–Dec 1; Honolulu. New York: IEEE; 2018. p. 1–7.
4. Senderovich A, Weidlich M, Gal A, Mandelbaum A. Queue mining for delay prediction in multi-class service processes. Information Systems 2015; 53: 278–295. doi: 10.1016/j.is.2015.03.010.
5. Rogge-Solti A, Weske M. Prediction of business process durations using non-Markovian stochastic Petri nets. Information Systems 2015; 54: 1–14. doi: 10.1016/j.is.2015.04.004.
6. Verenich I, Nguyen H, Rosa ML, et al. White-box prediction of process performance indicators via flow analysis. In: Proceedings of International Conference on Software and System Process; 2017 Jul 5–7; France. New York: Association for Computing Machinery; 2017. p. 85–94.
7. Tax N, Verenich I, Rosa ML, et al. Predictive business process monitoring with LSTM neural networks. In: Proceedings of International Conference on Advanced Information Systems Engineering; Jun 12–16; Essen. Berlin: Springer; 2017. p. 477–492.
8. Jimenez-Ramirez A, Barba I, Fernandez-Olivares J, et al. Time prediction on multi-perspective declarative business processes. Knowledge and Information Systems 2018; 57(3): 655–684. doi: 10.1007/s10115-018-1180-3.
9. Senderovich A, Di Francescomarino C, Ghidini C, et al. Intra and inter-case features in predictive process monitoring: A tale of two dimensions. In: Proceedings of International Conference on Business Process Management; 2017 Sept 10–15; Barcelona. Berlin: Springer; 2017. p. 306–323.
10. Folino F, Guarascio M, Pontieri L. Mining predictive process models out of low-level multidimensional logs. In: Proceedings of International Conference on Advanced Information Systems Engineering; 2014 Jun 16–20; Thessaloniki. Berlin: Springer; 2014. p. 533–547.
11. Jiménez-Ramírez A, Barba I, Del Valle C, et al. Generating multi-objective optimized business process enactment plans. In: Proceedings of International Conference on Advanced Information Systems Engineering; 2013 Jun 17–21; Valencia. Berlin: Springer; 2013. p. 99–115.
12. Rogge-Solti A, Mans RS, der Aalst WMPV, et al. Repairing event logs using timed process models. In: Proceedings of OTM Confederated International Conferences on the Move to Meaningful Internet Systems; 2013 Sept 9–13; Graz. Berlin: Springer; 2013. p. 705–708.
13. Polato M, Sperduti A, Burattin A, de Leoni M. Time and activity sequence prediction of business process instances. Computing 2018; 100(9): 1005–1031. doi: 10.1007/s00607-018-0593-x.
14. Verenich I, Dumas M, Rosa ML, et al. Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Transactions on Intelligent Systems and Technology (TIST) 2019; 10(4): 1–34. doi: 10.1145/3331449.
DOI: https://doi.org/10.32629/jai.v6i1.639
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