Abstract
As a new cloud service for delivering complex business applications, Business Process as a Service (BPaaS) is another challenge faced by cloud service platforms recently. To effectively reduce the security risk caused by business process execution load in BPaaS, it is necessary to detect the non-compliant process executions (instances) from tenants in advance by checking and monitoring the conformance of the executing process instances in real-time. However, the vast majority of existing conformance checking techniques can only be applied to the process instances that have been executed completely offline and only focus on the conformance from the single control-flow perspective. We develop an extensible multi-perspective conformance measurement method to address these issues first and then investigate the predictive conformance monitoring approach by automatically constructing an online multi-perspective conformance prediction model based on deep learning techniques. In addition, to capture more decisive features in the model from both local information and long-distance dependency within an executed process instance, we propose an approach, called CNN-BiGRU, by combining Convolutional Neural Network (CNN) with a variant and enhancement of Recurrent Neural Network (RNN). Extensive experiments on two data sets demonstrate the effectiveness and efficiency of the proposed CNN-BiGRU.
Article PDF
Similar content being viewed by others
Data Availability
The experiment data supporting this experiment analysis are from the website (https://researchdata.4tu.nl/home/), which has been described in a footnote to the article. The experiment data used to support the findings of this study are included in the article. The experiment data are described in Section 5 in detail. And the source code is available in Github (https://github.com/jiaojiaowang1992/multi-perspective_conformance-oriented_PPM.git).
References
Cusumano, M.: Cloud computing and saas as new computing platforms. Communation of the ACM 53(4), 27–29 (2010). https://doi.org/10.1145/1721654.1721667
Tsai, W.T., Bai, X.Y., Huang, Y.: Software-as-a-service (saas): perspectives and challenges. Science China Information Sciences 57(5), 1–15 (2014). https://doi.org/10.1007/s11432-013-5050-z
Sun, Y., Su, J., Yang, J.: Separating execution and data management: A key to business-process-as-a-service (bpaas). In: Sadiq, S., Soffer, P., Völzer, H. (eds.) Business Process Management, pp. 374–382. https://doi.org/10.1007/978-3-319-10172-9_25 (2014)
Bentounsi, M., Benbernou, S., Atallah, M.J.: Security-aware business process as a service by hiding provenance. Computer Standards and Interfaces 44, 220–233 (2016). https://doi.org/10.1016/j.csi.2015.08.011
Woitsch, R., Hinkelmann, K., Ferrer, A.M.J., Yuste, J.I.: Business process as a service (bpaas): The smart bpaas design environment CAiSE 2016 Industry Track. https://doi.org/10.26041/fhnw-1020 (2016)
Gzik, T.: Business process as a service - a systematic literature review. Towards Industry 4.0—Current Challenges in Information Systems, pp. 163–181. https://doi.org/10.1007/978-3-030-40417-8_10 (2020)
Qi, M., Wang, Y., Xiang, J., Li, T.: A correctness checking approach for collaborative business processes in the cloud. Science China Information Sciences, pp. 2020 (2020)
Verenich, I.: A general framework for predictive business process monitoring. In: Proceedings of CAiSE 2016 Doctoral Consortium, pp. 1–9. http://ceur-ws.org/Vol-1603/10000053.pdf (2016)
Burattin, A., Carmona, J.: A framework for online conformance checking. In: International Conference on Business Process Management, pp. 165–177. Springer. https://doi.org/10.1007/978-3-319-74030-0_12 (2017)
Burattin, A., van Zelst, S.J., Armas-Cervantes, A., van Dongen, B.F., Carmona, J.: Online conformance checking using behavioural patterns. In: International Conference on Business Process Management, pp. 250–267. Springer. https://doi.org/10.1007/978-3-319-98648-7_15 (2018)
van Zelst, S.J., Bolt, A., Hassani, M., van Dongen, B.F., Van der Aalst, W.M.P.: Online conformance checking: relating event streams to process models using prefix-alignments. International Journal of Data Science and Analytics 8(3), 269–284 (2019). https://doi.org/10.1007/s41060-017-0078-6
Song, W., Xia, X., Jacobsen, H.-A., Zhang, P., Hu, H.: Efficient alignment between event logs and process models. IEEE Trans. Serv. Comput. 10(1), 136–149 (2016). https://doi.org/10.1109/TSC.2016.2601094
de Leoni, M., Marrella, A.: Aligning real process executions and prescriptive process models through automated planning. Expert Syst. Appl. 82, 162–183 (2017). https://doi.org/10.1016/j.eswa.2017.03.047
García-Bañuelos, L., Van Beest, N.R.T.P., Dumas, M., La Rosa, M., Mertens, W.: Complete and interpretable conformance checking of business processes. IEEE Trans. Softw. Eng. 44(3), 262–290 (2017). https://doi.org/10.1109/TSE.2017.2668418
Leemans, S.J.J., Fahland, D., Van der Aalst, W.M.P.: Scalable process discovery and conformance checking. Softw. Syst. Model. 17(2), 599–631 (2018). https://doi.org/10.1007/s10270-016-0545-x
Dunzer, S., Stierle, M., Matzner, M., Baier, S.: Conformance checking: a state-of-the-art literature review. In: Proceedings of the 11th International Conference on Subject-Oriented Business Process Management, pp. 1–10. https://doi.org/10.1145/3329007.3329014 (2019)
Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with lstm neural networks. In: International Conference on Advanced Information Systems Engineering, pp. 477–492. Springer. https://doi.org/10.1007/978-3-319-59536-8_30 (2017)
Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: Review and benchmark. ACM Transactions on Knowledge Discovery from Data (TKDD) 13(2), 1–57 (2019). https://doi.org/10.1145/3301300
Mehdiyev, N., Evermann, J., Fettke, P.: A novel business process prediction model using a deep learning method. Business and Information Systems Engineering 62(2), 143–157 (2020). https://doi.org/10.1007/s12599-018-0551-3
Weytjens, H., De Weerdt, J.: Process outcome prediction: Cnn vs. lstm (with attention). In: International Conference on Business Process Management, pp. 321–333. Springer. https://doi.org/10.1007/978-3-030-66498-5_24 (2020)
Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: 2019 International Conference on Process Mining (ICPM), pp. 129–136. IEEE. https://doi.org/10.1109/ICPM.2019.00028 (2019)
Kratsch, W., Manderscheid, J., Röglinger, M., Seyfried, J.: Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction. Business Information System Engineering 63(3), 261–276 (2021). https://doi.org/10.1007/s12599-020-00645-0
Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. In: Kambhampati, S. (ed.) Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence IJCAI, pp. 2873–2879. IJCAI/AAAI Press. https://www.ijcai.org/Proceedings/16/Papers/408.pdf (2016)
Wang, J., Yu, L.-C., Robert Lai, K., Zhang, X.: Dimensional sentiment analysis using a regional cnn-lstm model. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 225–230. https://www.aclweb.org/anthology/P16-2037.pdf (2016)
Wang, S., Huang, M., Deng, Z.: Densely Connected Cnn with Multi-Scale Feature Attention for Text Classification. In: IJCAI, pp. 4468–4474. https://doi.org/10.24963/ijcai.2018/621 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259. https://www.aclweb.org/anthology/W14-4012.pdf (2014)
Shewalkar, A., Nyavanandi, D., Ludwig, S.A.: Performance evaluation of deep neural networks applied to speech recognition: Rnn, lstm and gru. Journal of Artificial Intelligence and Soft Computing Research 9(4), 235–245 (2019). https://doi.org/10.2478/jaiscr-2019-0006
Rozinat, A., Van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008). https://doi.org/10.1016/j.is.2007.07.001
Adriansyah, A., Sidorova, N., van Dongen, B.F.: Cost-based fitness in conformance checking. In: 2011 15Th International Conference on Application of Concurrency to System Design, pp. 57–66. IEEE. https://doi.org/10.1109/ACSD.2011.19 (2011)
Munoz-Gama, J., Carmona, J., Van der Aalst, W.M.P.: Conformance checking in the large: Partitioning and topology. In: Business Process Management, pp. 130–145. Springer. https://doi.org/10.1007/978-3-642-40176-3_11 (2013)
Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016). https://doi.org/10.1016/j.eswa.2016.08.040
Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Process diagnostics using trace alignment: opportunities, issues, and challenges. Inf. Syst. 37(2), 117–141 (2012). https://doi.org/10.1016/j.is.2011.08.003
Mannhardt, F., De Leoni, M., Reijers, H.A., van der Aalst, W.M.P.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016). https://doi.org/10.1007/s00607-015-0441-1
Alizadeh, M., Lu, X., Fahland, D., Zannone, N., van der Aalst, W.M.P.: Linking data and process perspectives for conformance analysis. Computers and Security 73, 172–193 (2018). https://doi.org/10.1016/j.cose.2017.10.010
De Leoni, M., Van Der Aalst, W.M.P., Van Dongen, B.F.: Data-and resource-aware conformance checking of business processes. In: International Conference on Business Information Systems, pp. 48–59. Springer. https://doi.org/10.1007/978-3-642-30359-3_5 (2012)
De Leoni, M., Van Der Aalst, W.M.P.: Aligning event logs and process models for multi-perspective conformance checking: An approach based on integer linear programming. In: Business Process Management, pp. 113–129. Springer. https://doi.org/10.1007/978-3-642-40176-3_10 (2013)
Van der Aalst, W., Adriansyah, A., van Dongen, B.: Replaying history on process models for conformance checking and performance analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(2), 182–192 (2012). https://doi.org/10.1002/widm.1045
Burattin, A.: Online conformance checking for petri nets and event streams. In: 15Th International Conference on Business Process Management (BPM 2017). https://core.ac.uk/download/pdf/97180593.pdf (2017)
Rogge-Solti, Andreas, Weske, Mathias: Prediction of business process durations using non-markovian stochastic petri nets. Inf. Syst. 54, 1–14 (2015). https://doi.org/10.1016/j.is.2015.04.004
Appice, Annalisa, Mauro, Nicola Di, Malerba, Donato: Leveraging shallow machine learning to predict business process behavior. In: 2019 IEEE International Conference on Services Computing (SCC), pp. 184–188. IEEE. https://doi.org/10.1109/SCC.2019.00039 (2019)
Harl, Maximilian, Weinzierl, Sven, Stierle, Mathias, Matzner, Martin: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst., pp. 1–16. https://doi.org/10.1080/12460125.2020.1780780 (2020)
Maria Maggi, Fabrizio, Di Francescomarino, Chiara, Dumas, Marlon, Ghidini, Chiara: Predictive monitoring of business processes. In: International conference on advanced information systems engineering, pp. 457–472. Springer. https://doi.org/10.1007/978-3-319-07881-6_31 (2014)
Lakshmanan, Geetika T, Shamsi, Davood, Doganata, Yurdaer N, Unuvar, Merve, Khalaf, Rania: A markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42(1), 97–126 (2015). https://doi.org/10.1007/s10115-013-0697-8
Leontjeva, Anna, Conforti, Raffaele, Di Francescomarino, Chiara, Dumas, Marlon, Maria Maggi, Fabrizio: Complex symbolic sequence encodings for predictive monitoring of business processes. In: International Conference on Business Process Management, pp. 297–313. Springer. https://doi.org/10.1007/978-3-319-23063-4_21 (2016)
Ferilli, Stefano, Esposito, Floriana, Redavid, Domenico, Angelastro, Sergio: Extended process models for activity prediction. In: International Symposium on Methodologies for Intelligent Systems, pp. 368–377. Springer. https://doi.org/10.1007/978-3-319-60438-1_36 (2017)
Taymouri, F., La Rosa, M., Erfani, S.M., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: The case of next event prediction. In: International Conference on Business Process Management, vol. 12168, pp. 237–256. Springer. https://doi.org/10.1007/978-3-030-58666-9_14 (2020)
Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: Processtransformer: Predictive business process monitoring with transformer network. arXiv:2104.00721 (2021)
Park, G., Song, M.: Predicting performances in business processes using deep neural networks. Decis. Support. Syst. 129, 113191 (2020). https://doi.org/10.1016/j.dss.2019.113191
Elman, J.L.: Finding structure in time. Cognitive Science 14(2), 179–211 (1990). https://doi.org/10.1207/s15516709cog1402_1
Olson, R.S., La Cava, W., Mustahsan, Z., Varik, A., Moore, J.H.: Data-driven advice for applying machine learning to bioinformatics problems. Pac Symp Biocomput, 23. https://doi.org/10.1142/9789813235533_0018 (2018)
De Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016). https://doi.org/10.1016/j.is.2015.07.003
Senderovich, A., Di Francescomarino, C., Ghidini, C., Jorbina, K., Maggi, F.M.: Intra and inter-case features in predictive process monitoring: A tale of two dimensions. In: International Conference on Business Process Management, pp. 306–323. Springer. https://doi.org/10.1007/978-3-319-65000-5_18 (2017)
Andrew, P.: Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997). https://doi.org/10.1016/S0031-3203(96)00142-2
Bergstra, James, Bengio, Yoshua: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012). https://doi.org/10.5555/2503308.2188395
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006). https://doi.org/10.5555/1248547.1248548
Acknowledgments
This work is supported by the Natural Science Foundation of China (No. 62002316), the VC Research (VCR 000067) for Prof. Chang, the Key Research and Development Program of Zhejiang Province (No. 2019C03138), the Key Science and Technology Project of Zhejiang Province (No. 2017C01010), and Zhejiang Provincial Natural Science Foundation (No. LQ20F020017).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Wang, J., Chang, V., Yu, D. et al. Conformance-oriented Predictive Process Monitoring in BPaaS Based on Combination of Neural Networks. J Grid Computing 20, 25 (2022). https://doi.org/10.1007/s10723-022-09613-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10723-022-09613-2