Modelling and classification of heteromorphic data on folk arts in digital information and educational environment

The authors offer the model of integration and classification of various formats of data related to the folk culture within academic digital information educational environment. In the case study of Moscow State Institute of Culture, they examine the objects and digital resources generated by the related departments (departments of folk culture, folk dance, folk musical instruments, folk singing art, design, and art and craft department). The highly heterogeneous data comprise texts, audio and video materials, graphics, music scores, 3D-models, and metainformation. As the methodological foundation, the authors propose the complex solution combining specialized ontology for semantic binding of heterogeneous objects, system architecture for long-term data preservation, and the model for their integration into the interactive 3D-space. The 3D-environment model, as a virtual museum or laboratory, enables to group and represent cultural objects by region or subject, and to visualize and enhance interdisciplinary ties. The novelty of the study is related to building Digital Information and Educational Environment (DIEE) model, integrated and adapted to the data specific character, i.e. folk culture and art. The study findings will be useful for digital heritage preservation, and improvement of research performance, and may be implemented into the learning process in the institutes of culture.

1. Tiutiunnik V. M., Dubrovin A. D. Intellektual`ny`e informatcionny`e sistemy` : uchebnik. 2-e izd., ster. Tambov i dr. : Izd-vo MINTC «Nobelistika», 2012. 356 s.

2. Vinogradskaia M. Iu., Kriazheva E. V., Saldaeva A. A. Analiz komponentov e`lektronnoi` obrazovatel`noi` sredy` vuza // Problemy` sovremennogo pedagogicheskogo obrazovaniia. 2023. № 78–2. S. 45–51.

3. Tyutyunnik V. M., Alguzo M. M. S. Decision-making models under conditions of uncertainly of formation, description and intellectual analysis of complex data files // Mathematics and Systems Science. 2025. Vol. 3, Issue 2. Article ID: 3053. P. 1–16. DOI: 10.54517/mss3053.

4. Dobud`ko T. V., Malova N. V., Pugach O. I. Geterogennaia e`lektronnaia informatcionnoobrazovatel`naia sreda vuza – problema ili vozmozhnost`? // Samarskii` nauchny`i` vestneyk. 2020. № 4 (33). S. 112–118.

5. Larina T. B. Ob ocenke kachestva interfei`sov e`lektronny`kh uchebny`kh kursov // Informatika i obrazovanie. 2021. № 6. S. 11–17.

6. Common data quality elements for health information systems: a systematic review / H. Ghalavand, S. Shirshahi, A. Rahimi et al. // BMC Medical Informatics and Decision Making. 2024. Vol. 24. P. 243.

7. Tulupov D. Iu., Tiutiunnik V. M. Adaptivny`i` spektral`no-inertcionny`i` sloi` na osnove obuchaemoi` maski Fur`e-preobrazovaniia dlia fil`tratcii vy`hodny`kh danny`kh v zadachakh prognozirovaniia fiziologicheskikh vremenny`kh riadov // Vestneyk Rossii`skogo novogo universiteta. Seriia: Slozhny`e sistemy`: modeli, analiz i upravlenie. 2025. № 3. S. 13–29. DOI: 10.61260/2218-13Х-2025-3-13-29.

8. Aljawarneh S., Lara J. A. Special Issue on Big Data and IoT in Education // Journal of Grid Computing. 2020. Vol. 18, No. 1. P. 1–2.

9. Petter S., DeLone W., McLean E. Measuring information systems success: models, dimensions, measures, and interrelationships // European Journal of Information Systems. 2008. Vol. 17. P. 236–263.

10. Al` Karkhi M. M. Analiticheskie i protcedurny`e modeli informatcionnogo poiska v mnogofaktorny`kh potokakh chastichno skry`ty`kh danny`kh : dis. … kand. tekhn. nauk : 05.25.05. Tambov, 2023. 134 s.

11. Laumer S., Maier C., Weitzel T. Information quality, user satisfaction, and the manifestation of workarounds: a qualitative and quantitative study of enterprise content management system users // European Journal of Information Systems. 2017. Vol. 26. P. 333–360.

12. The Data Lake Architecture: A Survey / C. Giebler, C. Gröger, E. Hoos et al. // IEEE Transactions on Knowledge and Data Engineering. 2021. Vol. 33, No. 12. P. 1–20.

13. Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality / J. Wang, Y. Liu, P. Li et al. // Journal of the Knowledge Economy. 2024. Vol. 15. P. 1159–1178.

14. López-Zambrano J., Lara J. A., Romero C. Toward an Ontology for Modeling Human Learning and Performance // IEEE Transactions on Learning Technologies. 2021. Vol. 14, No. 5. P. 585–599.

15. Sumin V. I., Grachev E. D., Gromov Iu. Iu. Matematicheskie modeli opredeleniia vremeni obrabotki zaprosov na serverakh informatcionny`kh sistem spetcial`nogo naznacheniia // Nauchno-tekhnicheskaia informatciia. Seriia 2: Informatcionny`e protcessy` i sistemy`. 2023. № 10. S. 11–15.

16. Kaznin A. A. Immersivny`e tekhnologii v obrazovanii: teoriia i praktika. Moskva : Tcifrovoe obrazovanie, 2022. 215 s.

17. Seroshtanova N. Iu. Prostranstvennaia organizatciia znanii` v virtual`ny`kh obrazovatel`ny`kh sredakh // Pedagogicheskaia informatika. 2023. № 1. S. 45–52.

18. Bova V. V. Tcifrovy`e dvoi`niki kul`turnogo naslediia: metody` i tekhnologii. Sankt-Peterburg : Kul`turny`i` kod, 2021. 189 s.

19. Gribova V. V. Semanticheskie tekhnologii v tcifrovoi` kul`ture. Moskva : Akademicheskii` proekt, 2023. 278 s.

20. Katurkin G. V. Upravlenie tcifrovy`mi aktivami v uchrezhdeniiakh kul`tury`. Ekaterinburg : Ural`skii` universitet, 2022. 167 s.

21. Dudarov S. P. Optimizatciia 3D-grafiki dlia veb-sred // Komp`iuternaia grafika i dizai`n. 2023. № 4. S. 23–29.