Using Structural and Parametric Polymorphism in the Creation of Digital Twins

  • V. N. ShvedenkoEmail author
  • V. V. ShvedenkoEmail author
  • O. V. Shchekochikhin
Information Analysis


This article considers digital twin creation based on structural and parametric polymorphism together with decision table ensembles. A new view of the concept of polymorphism applied to building digital models of physical objects is described. A new approach is proposed for using tables as means of designing digital twins by treating data flows and forming control signals to objects in the engineering system that are defined with metric-system indicator values.


structural polymorphism parametric polymorphism decision tables digital model digital shadow digital twin 


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  1. 1.
    Grieves, M., Digital Twin: Manufacturing Excellence through Virtual Factory Replication, 2014. Accessed December 10, 2018.
  2. 2.
    Negri, E., Fumagalli, L., and Macchi, M., A review of the roles of digital twin in CPS-based production systems, 27th International Conference on Flexible Automation and Intelligent Manufacturing—FAIM2017 (27–30 june 2017, Modena, Italy), Modena, 2017, no. 11, pp. 939–948.Google Scholar
  3. 3.
    Boschert, S. and Rosen, R., Digital twin—the simulation aspect, in Mechatronic Futures, Hehenberger, P. and Bradley, D., Eds., Cham: Springer, 2016, pp. 59–74.Google Scholar
  4. 4.
    Rosen, R., von Wichert, G., Lo, G., and Bettenhausen, K.D., About the importance of autonomy and digital twins for the future of manufacturing, IFACPapersOnLine, 2017, vol. 2015, no. 48, pp. 3–567.Google Scholar
  5. 5.
    Schluse, M. and Rossmann, J., From simulation to experimentable digital twins—simulation based development and operation of complex technical systems, Second IEEE International Symposium on Systems Engineering—ISSE2016 (October 3–5, Edinburgh, Scotland), Edinburgh, 2016, pp. 273–278.Google Scholar
  6. 6.
    Kraft, E.M., The air force digital thread/digital twin—life cycle integration and use of computational and experimental knowledge, 54th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum (AIAA 2016-0897), San Diego, 2016, pp. 1–22.Google Scholar
  7. 7.
    Abramovici, M., Göbel, J.C., and Dang, H.B., Semantic data management for the development and continuous reconfiguration of smart products and systems, CIRP Ann. Manuf. Technol., 2017, vol. 2016, no. 65, pp. 1–185.Google Scholar
  8. 8.
    Schvedenko, V.N., Schvedenko, V.V., and Shchekochikhin, O.V., Using structural polymorphism in creating process-based management information systems, Autom. Doc. Math. Linguist., 2017, vol. 2018, no. 52, pp. 6–290.Google Scholar
  9. 9.
    Bo Huang, An object model with parametric polymorphism for dynamic segmentation, J. Geogr. Inf. Sci., 2017, vol. 2003, no. 17, pp. 4–343.Google Scholar
  10. 10.
    Mezini, M. and Ostermann, K., Variability management with feature-oriented programming and aspects, Proceedings of the 12th ACM SIGSOFT Symposium on Foundations of Software Engineering, New York, 2004, pp. 127–136.Google Scholar
  11. 11.
    Common Object Request Broker Architecture (CORBA), v2.4.2. Revision 2.4 (February 2001), OMG Specification, 2001, p. 3–1. Accessed December 10, 2018.
  12. 12.
    Dragan, L. and Watt, S.M., Parametric polymorphism optimization for deeply nested types in computer algebra, Maple Summer Workshop, Waterloo (Canada), 2005, pp. 243–259.Google Scholar
  13. 13.
    Gesbert, N., Genevès, P., and Layaïda, N., Parametric polymorphism and semantic subtyping: The logical connection, Proceedings of the 16th ACM SIGPLAN International Conference on Functional Programming—ICFP 2011 (September 19–21, 2001, Tokyo, Japan), Tokyo, 2001, pp. 107–116.Google Scholar
  14. 14.
    Vouillon, J., Polymorphic regular tree types and patterns, POPL’06: Conference Record of the 33rd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, New York, 2006, pp. 103–114.Google Scholar
  15. 15.
    Dragan, L. and Watt, S.M., Performance analysis of generics in scientific computing, Proc. 7th International Symposium on Symbolic and Numeric Algorithms in Scientific Computing—SYNASC 2005 (Sept. 25–29 2005, Timisoara, Romania), Los Alamitos, CA, 2005, pp. 93–100.Google Scholar
  16. 16.
    Castagna, G. and Xu, Z., Set-theoretic foundation of parametric polymorphism and subtyping, Proceedings of the 16th ACM SIGPLAN International Conference on Functional Programming (September 19–21, 2011, Tokyo, Japan), Tokyo, 2011, pp. 94–106.Google Scholar
  17. 17.
    Oancea, C.E. and Watt, S.M., Parametric polymorphism for software component architectures, Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications—OOPSLA 2005 (October 16–20, 2005, San Diego, CA, USA), New York, 2005.Google Scholar
  18. 18.
    Jagadeesan, R., Jeffrey, A., and Riely, J., Typed parametric polymorphism for aspects, Sci. Comput. Progr., 2017, vol. 2006, no. 63, pp. 3–26.zbMATHGoogle Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.All-Russian Institute for Scientific and Technical InformationMoscowRussia
  2. 2.OOO Regul+St. PetersburgRussia
  3. 3.OOO MMTRKostromaRussia

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