Information Technologies of Modeling Processes for Preparation of Professionals in Smart Cities

  • Andrii Bomba
  • Nataliia Kunanets
  • Mariia Nazaruk
  • Volodymyr Pasichnyk
  • Nataliia Veretennikova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

It is proposed the training process of qualified specialists in accordance with the needs of a person and the requirements of the labor market in the smart city to be presented in the form of five consecutive functional stages: determination of professional inclinations and abilities; monitoring of the urban labor market; a choice of the future profession; a choice of educational institution; formation of an individual learning trajectory. The model of the data analysis process of career orientation testing with obvious uncertainty and hidden redundancy is presented. For storing and analyzing big data, it is suggested to use data warehouses and the model of data warehouse of the complex assessment of educational institution activities is provided. The diffusion-liked model of the multicomponent knowledge potential dissemination is described and variants of the problem solution of identifying the component parameters of the knowledge potential are described, with the aim of their further usage for the formation of individual learning trajectories. The architecture of the software and algorithmic complex of information and technological support of the processes for specialist training in the smart city is developed.

Keywords

Smart city Big data Data warehouse Knowledge potential Diffusion-liked model Career orientation test 

References

  1. 1.
    Bouskela, M., Casseb, M., Bassi, S., De Luca, C., Facchina, M.: The Road Toward Smart Cities: Migrating from Traditional City Management to the Smart City: Monograph. Inter-American Development Bank, Washington (2016)CrossRefGoogle Scholar
  2. 2.
    Kupriyanovsky, V.P., Bulancha, S.A., Chernykh, K.Y., Namiot, D.E.: Smart cities as the “capitals” of the digital economy. Int. J. Open Inf. Technol. 2, 41–52 (2016). (in Russian)Google Scholar
  3. 3.
    Boulton, A., Brunn, S.D., Devriendt, L.: Cyberinfrastructures and “smart” world cities: physical, human, and soft infrastructures. In: Taylor, P., Derudder, B., Hoyler, M., Witlox, F. (eds.) International Handbook of Globalization and World Cities. Edward Elgar, Cheltenham (2012)Google Scholar
  4. 4.
    Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18(2), 65–82 (2011).  https://doi.org/10.1080/10630732CrossRefGoogle Scholar
  5. 5.
    Carey, K.: The End of College: Creating the Future of Learning and the University of Everywhere. Riverhead Books, New York (2015)Google Scholar
  6. 6.
    Will universities be responsible for the success of cities? https://www.ecampusnews.com/campus-administration/universities-smart-cities/
  7. 7.
    Plumb, D., Leverman, A., Gray, R.: The learning city in ‘planet of slums’. Stud. Contin. Educ. 29(1), 37–50 (2007)CrossRefGoogle Scholar
  8. 8.
    Liu, D., Huang, R., Wosinski, M.: Smart Learning in Smart Cities. LNET. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-4343-7CrossRefGoogle Scholar
  9. 9.
    Haidine, A., Aqqal, A., Ouahmane, H.: Evaluation of communications technologies for smart grid as part of smart cities. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds.) Proceedings of the Mediterranean Conference on Information and Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol. 381, pp. 277–285. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-30298-0_29CrossRefGoogle Scholar
  10. 10.
    Concilio, G., Marsh, J., Molinari, F., Rizzo, F.: Human smart cities: a new vision for redesigning urban community and citizen’s life. In: Skulimowski, A., Kacprzyk, J. (eds.) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol. 364, pp. 269–278. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-19090-7_21CrossRefGoogle Scholar
  11. 11.
    Scuotto, V., Ferraris, A., Bresciani, S.: Internet of things: applications and challenges in smart cities: a case study of IBM smart city projects. Bus. Process Manag. J. 22(2), 357–367 (2016)CrossRefGoogle Scholar
  12. 12.
    Holland, J.: Making Vocational Choices: A Theory of Careers. Prentice Hall, Upper Saddle River (1973)Google Scholar
  13. 13.
    Nikolskyi, Y.: Model of data analysis process. Comput. Sci. Inf. Technol. 663, 108–116 (2010)Google Scholar
  14. 14.
    Clifford, L.: Big data: how do your data grow? Nature 455, 28–29 (2008).  https://doi.org/10.1038/455028aCrossRefGoogle Scholar
  15. 15.
    Jacobs, A.: The pathologies of big data. Databases 7(6), 1–12 (2009)Google Scholar
  16. 16.
    Inmon, W.: Corporate Information Factory, 3rd edn. Wiley, New York (2000)Google Scholar
  17. 17.
    Chai, H., Wu, G., Zhao, Y.: A document-based data warehousing approach for large scale data mining. In: Zu, Q., Hu, B., Elçi, A. (eds.) ICPCA/SWS 2012. LNCS, vol. 7719, pp. 69–81. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37015-1_7CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Dilawar, M.U., Syed, F.A.: Mathematical modeling and analysis of network service failure in datacentre. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 6(6), 30–36 (2014).  https://doi.org/10.5815/ijmecs.2014.06.04CrossRefGoogle Scholar
  20. 20.
    Peleshko, D., Rak, T., Izonin, I.: Image superresolution via divergence matrix and automatic detection of crossover. Int. J. Intell. Syst. Appl. (IJISA) 8(12), 1–8 (2016).  https://doi.org/10.5815/ijisa.2016.12.01CrossRefGoogle Scholar
  21. 21.
    Kotevski, Z., Tasevska, I.: Evaluating the potentials of educational systems to advance implementing multimedia technologies. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 9(1), 26–35 (2017).  https://doi.org/10.5815/ijmecs.2017.01.03CrossRefGoogle Scholar
  22. 22.
    Jian, G.: Reform of database course in police colleges based on working process. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 9(2), 41–46 (2017).  https://doi.org/10.5815/ijmecs.2017.02.05CrossRefGoogle Scholar
  23. 23.
    Bomba, A., Nazaruk, M., Kunanets, N., Pasichnyk, V.: Constructing the diffusion-liked model of biocomponent knowledge potential distribution. Int. J. Comput. 16(2), 74–81 (2017). (in Ukrainian)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Andrii Bomba
    • 1
  • Nataliia Kunanets
    • 2
  • Mariia Nazaruk
    • 2
  • Volodymyr Pasichnyk
    • 2
  • Nataliia Veretennikova
    • 2
  1. 1.Informatics and Applied Mathematics DepartmentState Humanitarian UniversityRivneUkraine
  2. 2.Information Systems and Networks DepartmentLviv Polytechnic National UniversityLvivUkraine

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