Cognitive Automation of Business Process Monitoring Using Digital Twins and Knowledge Graphs
Keywords:
Cognitive Automation, Business Process Monitoring, Digital Twins, Knowledge Graphs, Real-Time Analytics, Predictive Modeling, Semantic Technologies, Intelligent Decision-Making.Synopsis
This paper explores the integration of Cognitive Automation into Business Process Monitoring through the synergistic use of Digital Twins and Knowledge Graphs. As organizations strive for enhanced efficiency and adaptability, this approach offers a dynamic and intelligent framework for real-time monitoring, predictive analytics, and decision-making. By leveraging the real-time data synchronization capabilities of Digital Twins and the semantic richness of Knowledge Graphs, businesses can achieve a more holistic and actionable understanding of their processes.
References
[1] Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
[2] Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022.
[3] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.
[4] Anugula Sethupathy, U.K. (2021). Securing cloud-based streaming data platforms best practices and frameworks. International Research Journal of Modernization in Engineering Technology and Science, 3(11), 1516–1526. https://doi.org/10.56726/IRJMETS17179
[5] Bellomarini, L., Sallinger, E., & Gottlob, G. (2018). The Vadalog system: Datalog-based reasoning for knowledge graphs. Proceedings of the VLDB Endowment, 11(10), 1197–1200.
[6] Ehrlinger, L., & Wöß, W. (2016). Towards a definition of knowledge graphs. SEMANTiCS Posters & Demos.
[7] Chen, D., Xu, L. D., Lu, Y., Hu, B., & Wang, H. (2019). Smart factory of industry 4.0: Key technologies, application case, and challenges. IEEE Access, 6, 6505–6519.
[8] Udugama, I. A., Su, C., & Jiang, Q. (2021). Five levels of digital twins in process operations. Procedia CIRP, 98, 529–534.
[9] Azangoo, M., Taherkordi, A., Blech, J. O., & Vyatkin, V. (2021). Digital twin-assisted controlling of AGVs in flexible manufacturing environments. Journal of Manufacturing Systems, 60, 110–121.
[10] Mortlock, T., Muthirayan, D., Yu, S.-Y., Khargonekar, P. P., & Al Faruque, M. A. (2021). Graph learning for cognitive digital twins in manufacturing systems. IEEE Transactions on Industrial Informatics, 17(11), 7563–7572.
[11] Rožanec, J. M., Lu, J., Rupnik, J., Škrjanc, M., Mladenić, D., Fortuna, B., Zheng, X., & Kiritsis, D. (2021). Actionable cognitive twins for decision making in manufacturing. Computers & Industrial Engineering, 156, 107234.
[12] Anugula Sethupathy, U.K. (2020). Cloud-Native Architectures for Real-Time Retail Inventory and Analytics Platforms. International Journal of Novel Research and Development, 5(6), 339–355. https://doi.org/10.56975/ijnrd.v5i6.309063
[13] Jacobs, T., Yu, J., Gastinger, J., & Sztyler, T. (2021). ProcK: Machine learning for knowledge-intensive processes. arXiv preprint arXiv:2109.04881.
Published
Series
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.