Online incremental updating for model enhancement based on multi-perspective trusted intervals

References

• Al-Ali, H., Damiani, E., Al-Qutayri, M., Abu-Matar, M., & Mizouni, R. (2018). Translating BPMN to business rules. In P. Ceravolo, C. Guetl, & S. Rinderle-Ma (eds.), International symposium on data-driven process discovery and analysis (pp. 22–36). Springer International Publishing. https://doi.org/10.1007/978-3-319-74161-1_2
   Google Scholar
• Alizadeh, M., Lu, X., Fahland, D., Zannone, N., & van der Aalst, W. M. (2018, March). Linking data and process perspectives for conformance analysis. Computers & Security, 73, 172–193. https://doi.org/10.1016/j.cose.2017.10.010
   Web of Science ®Google Scholar
• Armas Cervantes, A., van Beest, N. R. T. P., La Rosa, M., Dumas, M., & García-Bañuelos, L. (2017). Interactive and incremental business process model repair. In On the move to meaningful internet systems. OTM 2017 conferences (pp. 53–74). Springer International Publishing.
   Google Scholar
• Augusto, A., Conforti, R., Dumas, M., La Rosa, M., & Polyvyanyy, A. (2019). Split miner: automated discovery of accurate and simple business process models from event logs. Knowledge and Information Systems, 59(2), 251–284. https://doi.org/10.1007/s10115-018-1214-x
   Web of Science ®Google Scholar
• Awad, A., Weidlich, M., & Sakr, S. (2020). Process mining over unordered event streams. In International conference on process mining (ICPM) (pp. 81–88). IEEE.
   Google Scholar
• Barbon Junior, S., Tavares, G. M., Da Costa, V. G. T., Ceravolo, P., & Damiani, E. (2018). A framework for human-in-the-loop monitoring of concept-drift detection in event log stream. In Companion proceedings of the the web conference 2018 (pp. 319–326). ACM Press.
   Google Scholar
• Bose, R. J. C., Van Der Aalst, W. M., V Z Liobait E, I. E., & Pechenizkiy, M. (2013). Dealing with concept drifts in process mining. IEEE Transactions on Neural Networks and Learning Systems, 25(1), 154–171. https://doi.org/10.1109/TNNLS.2013.2278313
   Web of Science ®Google Scholar
• Brons, D., Scheepens, R., & Fahland, D. (2021). Striking a new balance in accuracy and simplicity with the probabilistic inductive miner. In International conference on process mining (ICPM) (pp. 32–39). IEEE.
   Google Scholar
• Burattin, A., Cimitile, M., Maggi, F. M., & Sperduti, A. (2015). Online discovery of declarative process models from event streams. IEEE Transactions on Services Computing, 8(6), 833–846. https://doi.org/10.1109/TSC.2015.2459703
   Web of Science ®Google Scholar
• Burattin, A., & Josep, C. (2018). A framework for online conformance checking. In International conference on business process management (pp. 165–177). Springer International Publishing.
   Google Scholar
• Chan, N. N., Yongsiriwit, K., Gaaloul, W., & Mendling, J. (2014). Mining event logs to assist the development of executable process variants. In Advanced information systems engineering (Vol. 8484, pp. 548–563). Springer.
   Google Scholar
• Dees, M., de Leoni, M., & Mannhardt, F. (2017). Enhancing process models to improve business performance: A methodology and case studies. In H. Panetto, et al. (Eds.), On the move to meaningful internet systems. OTM 2017 conferences (pp. 232–251). Springer International Publishing.
   Google Scholar
• Deshmukh, S., Agarwal, M., Gupta, S., & Kumar, N. (2020). MOEA for discovering pareto-optimal process models: an experimental comparison. International Journal of Computational Science and Engineering, 21(3), 446. https://doi.org/10.1504/IJCSE.2020.106067
   Web of Science ®Google Scholar
• Dixit, P. M., Verbeek, H., Buijs, J., & Aalst, W. (2018). Interactive data-driven process model construction. In International conference on conceptual modeling (pp. 251–265). Springer International Publishing.
   Google Scholar
• Du, H., Hao, Y., & Wang, Z. (2021). An improved density peaks clustering algorithm by automatic determination of cluster centres. Connection Science, 34(1), 857–873. https://doi.org/10.1080/09540091.2021.2012422.
   Web of Science ®Google Scholar
• Fang, X. W., Cao, R., Liu, X., & Wang, L. (2018). A method of mining hidden transition of business process based on region. IEEE Access, 6, 25543–25550. https://doi.org/10.1109/ACCESS.2018.2833450
   Web of Science ®Google Scholar
• Fang, X. W., Li, J., Wang, L. L., & Fang, H. (2020). Log automaton under conditions of infrequent behaviour mining. International Journal of Information Technology and Management, 19(4), 292–304. https://doi.org/10.1504/IJITM.2020.110239
   Google Scholar
• Hamdad, L., Ournani, Z., Benatchba, K., & Bendjoudi, A. (2020). Two-level parallel CPU/GPU-based genetic algorithm for association rule mining. International Journal of Computational Science and Engineering, 22(2/3), 335. https://doi.org/10.1504/IJCSE.2020.107366
   Web of Science ®Google Scholar
• Kindler, E., Rubin, V., & Schäfer, W. (2006). Incremental workflow mining based on document versioning information. In Unifying the software process spectrum (pp. 287–301). Springer.
   Google Scholar
• Leno, V., Armas-Cervantes, A., Dumas, M., La Rosa, M., & Maggi, F. M. (2018). Discovering process maps from event streams. In Proceedings of the 2018 international conference on software and system process (pp. 86–95). ACM.
   Google Scholar
• Lu, K., Fang, X., Fang, N., & Asare, E. (2022). Discovery of effective infrequent sequences based on maximum probability path. Connection Science, 34(1), 63–82. https://doi.org/10.1080/09540091.2021.1951667
   Web of Science ®Google Scholar
• Lu, X., Gal, A., & Reijers, H. A. (2020). Discovering hierarchical processes using flexible activity trees for event abstraction. In International conference on process mining (pp. 145–152). IEEE.
   Google Scholar
• Marutho, D., Hendra Handaka, S., Wijaya, E., Muljono, (2018). The determination of cluster number at k-mean using Elbow method and purity evaluation on headline news. In International seminar on application for technology of information and communication (pp. 533–538). IEEE.
   Google Scholar
• Nguyen, H., Dumas, M., ter Hofstede, A. H. M., La Rosa, M., & Maggi, F. M. (2016). Business process performance mining with staged process flows. In S. Nurcan, P. Soffer, M. Bajec, & J. Eder (Eds.), Advanced information systems engineering (pp. 167–185). Springer International Publishing.
   Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.1007/978-1-4842-3913-1_8
   Web of Science ®Google Scholar
• Pika, A., Leyer, M., Wynn, M. T., Fidge, C. J., Ter Hofstede, A. H. M., & Van Der Aalst, W. M. P. (2017). Mining resource profiles from event logs. ACM Transactions on Management Information Systems, 8(1), 1–30. https://doi.org/10.1145/3041218
   Google Scholar
• Tavares, G. M., da Costa, V. G. T., Martins, V. E., Ceravolo, P., & Barbon, S. (2018). Anomaly Detection in Business Process Based on Data Stream Mining. In Proceedings of the XIV Brazilian symposium on information systems. Association for Computing Machinery.
   Google Scholar
• Thakur, H. K., Gupta, A., Nag, S., & Shrivastava, R. (2020). Multi-class instance-incremental framework for classification in fully dynamic graphs. International Journal of Computational Science and Engineering, 21(1), 69. https://doi.org/10.1504/IJCSE.2020.105214
   Web of Science ®Google Scholar
• van der Aalst, W. (2016). Process mining data science in action (2nd ed.). Springer.
   Google Scholar
• van Zelst, S. J., van Dongen, B. F., & van der Aalst, W. M. P. (2018). Event stream-based process discovery using abstract representations. Knowledge and Information Systems, 54(2), 407–435. https://doi.org/10.1007/s10115-017-1060-2
   Web of Science ®Google Scholar
• Wang, L. l., Fang, X. w., Asare, E., & Huan, F. (2021). An optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow. Scientific Programming, 2021, 1–17. https://doi.org/10.1155/2021/8874316.
   Web of Science ®Google Scholar
• Xie, J., Girshick, R., & Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In Proceedings of machine learning research (Vol. 48, pp. 478–487). PMLR.
   Google Scholar
• Xu, H., Zhang, S., Zhu, G., & Zhu, H. (2022). ALSEE: a framework for attribute-level sentiment element extraction towards product reviews. Connection Science, 34(1), 205–223. https://doi.org/10.1080/09540091.2021.1981825
   Web of Science ®Google Scholar
• Yongsiriwit, K., Chan, N. N., & Gaaloul, W. (2015). Log-based process fragment querying to support process design. In Hawaii international conference on system sciences (pp. 4109–4119). IEEE.
   Google Scholar
• Zhang, S., Yu, H., & Zhu, G. (2022). An emotional classification method of Chinese short comment text based on ELECTRA. Connection Science, 34(1), 254–273. https://doi.org/10.1080/09540091.2021.1985968
   Web of Science ®Google Scholar
• Zhu, G., Pan, Z., Wang, Q., Zhang, S., & Li, K. C. (2020). Building multi-subtopic bi-level network for micro-blog hot topic based on feature co-occurrence and semantic community division. Journal of Network and Computer Applications, 170, 1–10. https://doi.org/10.1016/j.jnca.2020.102815
   Web of Science ®Google Scholar