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Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems

Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Cognitive Twins (CT) are proposed as Digital Twins (DT) with augmented semantic capabilities for identifying the dynamics of virtual model evolution, promoting the understanding of interrelationships between virtual models and enhancing the decision-making based on DT. The CT ensures that assets of Internet of Things (IoT) systems are well-managed and concerns beyond technical stake holders are addressed during IoT system development. In this paper, a Knowledge Graph (KG) centric framework is proposed to develop CT. Based on the framework, a future tool-chain is proposed to develop the CT for the initiatives of H2020 project FACTLOG. Based on the comparison between DT and CT, we infer the CT is a more comprehensive approach to support IoT-based systems development than DT.

Keywords

  • Cognitive Twins
  • Decision-making
  • Knowledge Graph
  • Internet of Things

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Notes

  1. 1.

    H2020 Project FACTLOG: http://factlog.eu/.

References

  1. Chernyshev, M., Baig, Z., Bello, O., Zeadally, S.: Internet of Things (IoT): research, simulators, and testbeds. IEEE Internet Things J. 5, 1637–1647 (2017). https://doi.org/10.1109/JIOT.2017.2786639

    CrossRef  Google Scholar 

  2. Jin, J., Gubbi, J., Marusic, S., Palaniswami, M.: An information framework for creating a smart city through Internet of Things. IEEE Internet Things J. 1, 112–121 (2014). https://doi.org/10.1109/JIOT.2013.2296516

    CrossRef  Google Scholar 

  3. Bricogne, M., Le Duigou, J., Eynard, B.: Design Processes of Mechatronic Systems. In: Hehenberger, P., Bradley, D. (eds.) Mechatronic Futures, pp. 75–89. Springer, Cham (2016)

    Google Scholar 

  4. Grieves, M.: Digital Twin: Manufacturing Excellence Through Virtual Factory Replication (2014)

    Google Scholar 

  5. Qi, Q., Tao, F., Hu, T., et al.: Enabling technologies and tools for digital twin. J. Manuf. Syst. (2019). https://doi.org/10.1016/j.jmsy.2019.10.001

    CrossRef  Google Scholar 

  6. Tao, F., Zhang, M., Cheng, J., Qi, Q.: Digital twin workshop: a new paradigm for future workshop. Jisuanji Jicheng Zhizao Xitong/Comput. Integr. Manuf. Syst. CIMS (2017). https://doi.org/10.13196/j.cims.2017.01.001

  7. Cho, S., May, G., Kiritsis, D.: A semantic-driven approach for industry 4.0. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 347–354. IEEE (2019)

    Google Scholar 

  8. Kharlamov, E., Martin-Recuerda, F., Perry, B., et al.: Towards semantically enhanced digital twins. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4189–4193. IEEE (2018)

    Google Scholar 

  9. Ochoa, J.L., Valencia-García, R., Perez-Soltero, A., Barceló-Valenzuela, M.: A semantic role labelling-based framework for learning ontologies from Spanish documents. Expert Syst. Appl. 40, 2058–2068 (2013). https://doi.org/10.1016/j.eswa.2012.10.017

    CrossRef  Google Scholar 

  10. Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: CEUR Workshop Proceedings (2016)

    Google Scholar 

  11. Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 11–33 (2016). https://doi.org/10.1109/JPROC.2015.2483592

    CrossRef  Google Scholar 

  12. Rosen, R., Boschert, S., Sohr, A.: Next generation digital twin. atp Mag 60, 86 (2018). https://doi.org/10.17560/atp.v60i10.2371

  13. Gómez-Berbís, J.M., de Amescua-Seco, A.: SEDIT: semantic digital twin based on industrial IoT data management and knowledge graphs, pp. 178–188 (2019)

    Google Scholar 

  14. Banerjee, A., Dalal, R., Mittal, S., Joshi, K.P.: Generating digital twin models using knowledge graphs for industrial production lines. In: Workshop on Industrial Knowledge Graphs, Co-located with the 9th International ACM Web Science Conference 2017 (2017)

    Google Scholar 

  15. Tao, F., Zhang, H., Liu, A., Nee, A.Y.C.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15, 2405–2415 (2019). https://doi.org/10.1109/TII.2018.2873186

    CrossRef  Google Scholar 

  16. Minerva, R., Biru, A., Rotondi, D.: Towards a definition of the Internet of Things (IoT). IEEE Internet Initiat. (2015). https://doi.org/10.1111/j.1440-1819.2006.01473.x

    CrossRef  Google Scholar 

  17. Díaz, M., Martín, C., Rubio, B.: State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J. Netw. Comput. Appl. 67, 99–117 (2016). https://doi.org/10.1016/j.jnca.2016.01.010

    CrossRef  Google Scholar 

  18. Alaasam, A.B.A., Radchenko, G., Tchernykh, A., et al.: Scientific micro-workflows : where event-driven approach meets workflows to support digital twins. In: Proceedings of the International Conference on RuSCDays’18 - Russ Supercomput Days, Moscow, Russia, 24–25 September 2018, vol. 1, pp. 489–495. MSU (2018)

    Google Scholar 

  19. Director CSLNI of S and T: Integration Definition for Function Modeling (Idef0). Draft Federal Information Processing Standards Publication 183 (1993)

    Google Scholar 

  20. Smolander, K., Lyydnen, K., Tahvanalnen, V.-P., Marttiin, P.: MetaEdit - a flexible graphical environment for methodology modelling. In: Advanced Information Systems Engineering, pp. 168–193 (1991). https://doi.org/10.1007/3-540-54059-8_85

  21. van Beek, D.A., Fokkink, W.J., Hendriks, D., et al.: CIF 3: model-based engineering of supervisory controllers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 575–580 (2014)

    Google Scholar 

  22. Musen, M.A., Stevens, R.D.: The protege OWL experience. In: Proceedings of the OWLED, Workshop on OWL: Experiences and Directions (2005)

    Google Scholar 

  23. Berthold, M.R., Cebron, N., Dill, F., et al.: KNIME - the Konstanz information miner. ACM SIGKDD Explor. Newsl. 11, 26 (2009). https://doi.org/10.1145/1656274.1656280

    CrossRef  Google Scholar 

  24. Ramsundar, B.: TensorFlow Tutorial. CS224d (2016)

    Google Scholar 

  25. Simulink, M., Natick, M.A.: The mathworks. MATHWORKS (1993)

    Google Scholar 

  26. Petnga, L., Austin, M.: Ontologies of time and time-based reasoning for MBSE of cyber-physical systems. Procedia Comput. Sci. 16, 403–412 (2013). https://doi.org/10.1016/j.procs.2013.01.042

    CrossRef  Google Scholar 

  27. OASISOpenProject: Open Services for Lifecycle Collaboration Core Specification Version 3.0 (2018)

    Google Scholar 

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Acknowledgement

The work presented in this paper was supported by the EU H2020 project (869951) FACTLOG-Energy-aware Factory Analytics for Process Industries and EU H2020 project (825030) QU4LITY Digital Reality in Zero Defect Manufacturing and the InnoSwiss IMPULSE project on Digital Twins.

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Correspondence to Jinzhi Lu .

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Lu, J., Zheng, X., Gharaei, A., Kalaboukas, K., Kiritsis, D. (2020). Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems. In: Wang, L., Majstorovic, V., Mourtzis, D., Carpanzano, E., Moroni, G., Galantucci, L. (eds) Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-46212-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-46212-3_7

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