D.R. Nikolsky1
1 VSTU (Voronezh, Russia)

1 nikolsky.dan@bk.ru

Graph database management systems (DBMSs) have become quite widespread, but in the case of large databases with thousands of different types of objects and relationships, difficulties arise when using graph DBMSs. Metagraph models, which allow using several levels of nesting when constructing large, complex graphs, are a promising approach in this regard. Annotated metagraphs have the greatest generality. To implement a metagraph data model, it is necessary to develop a specialized metagraph DBMS.

The goal of the article is to build a data model based on an annotated metagraph (defining a set of necessary operations and data integrity criteria) for use in a DBMS based on this model.

A review of data models of existing graph DBMSs has been conducted. A data model for a metagraph DBMS has been formulated. Operations on data in this model and data integrity criteria have been defined.

The obtained results form the basis for the development and implementation of a general-purpose metagraph DBMS that implements a metagraph data model and does not have the disadvantages of previous graph DBMSs. Such a DBMS can be used in a wider range of projects compared to traditional graph DBMS.

Nikolsky D.R. Formal definition of a metagraph database data model. Neurocomputers. 2026. V. 28. № 1. P. 74–79. DOI: https://doi.org/10.18127/j19998554-202601-08 (in Russian)

1. Nikol'skij D.R., Barabanov V.F., Grebennikova N.I. i dr. Analiz grafovykh sistem upravleniya bazami dannykh. Vestnik Voronezhskogo gosudarstvennogo universiteta inzhenernykh tekhnologij. 2023. T. 19. № 6. S. 13–20. (in Russian)
2. Nikol'skij D.R., Barabanov V.F., Safronov V.V. i dr. Arkhitektura SUBD, ispol'zuyushchej metagrafovuyu model' dannykh. Vestnik Voronezhskogo gosudarstvennogo tekhnicheskogo universiteta. 2024. T. 20. № 2. S.29–34. (in Russian)
3. Chernenkiy V.M., Gapanyuk Y.E., Kaganov Y.T. et al. Storing metagraph model in relational, document-oriented, and graph data-bases. Selected Papers of the XX International Conference on Data Analytics and Management in Data Intensive Domains. 2018. V. 2277. P. 82–89.
4. Chernenkiy V.M., Gapanyuk Y.E., Revunkov G.I. et al. The principles and the conceptual architecture of the metagraph storage system. Communications in Computer and Information Science. 2019. V. 1003. P. 73–87.
5. Sukhobokov A.A., Trufanov V.A., Stolyarov Y.A. et al. Distributed metagraph DBMS based on blockchain technology. Natural and Technical Sciences. 2021. V. 7. P. 201–209.
6. Erokhin I.A., Grunin N.S., Molchanov A.V. et al. Method of storing metagraph data model in PostgreSQL DBMS. Proceedings of the All-Russian Scientific Conference «Artificial Intelligence in Management, Control, and Data Processing Systems». 2022. V. 1. P. 348–351.
7. Sukhobokov A.A., Gapanyuk Y.E., Vetoshkin A.A. et al. Universal data model as a way to build multi-paradigm data lakes. 9th International Conference on Big Data Analytics (ICBDA). IEEE. 2024. P. 203–211.
8. Codd E.F. Data models in database management. Proceedings of the 1980 Workshop on Data Abstraction, Databases and Conceptual Modeling. 1980. V. 11. № 2. P. 112–114.
9. Angles R. A comparison of current graph database models. IEEE 28th International Conference on Data Engineering Workshops. 2012. P. 171–177.
10. Ghrab A., Romero O., Skhiri S. et al. GRAD: On graph database modeling. arXiv preprint. 2016. arXiv:1602.00503.
11. Angles R., Hogan A., Lassila O. et al. Multilayer graphs: a unified data model for graph databases. Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). 2022. P. 1–6.
12. Basu A., Blanning R.W. Metagraphs and their applications. NY: Springer New York. 2007.
13. Chernen'kij V.M., Gapanyuk Yu.E., Revunkov G.I. i dr. Metagrafovyj podkhod dlya opisaniya gibridnykh intellektual'nykh informatsionnykh system. Prikladnaya informatika. 2017. T. 12. № 3 (69). S. 57–79. (in Russian)
14. Chernenkiy V.M., Gapanyuk Y.E., Revunkov G.I. et al. Using metagraph approach for complex domains description. Collection of Scientific Papers of the XIX International Conference «Data analytics and management in data intensive domains». 2017. P. 420–427.