Comparative Analysis of Theoretical and Practical Approaches to Multi-Model Query Processing: Current Limitations and Research Directions
( Pp. 34-50)
More about authors
Volkov Artyom S.
postgraduate student
Bauman Moscow State Technical University
Moscow, Russian Federation Vinogradova Maria V. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation Afanasyev Gennady I. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation
Bauman Moscow State Technical University
Moscow, Russian Federation Vinogradova Maria V. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation Afanasyev Gennady I. Cand. Sci. (Eng.), Associate Professor; associate professor; Bauman Moscow State Technical University; Moscow, Russian Federation
Abstract:
Modern data processing systems face challenges when integrating heterogeneous data sources (structured, semi-structured, and unstructured). This work aims to analyze and compare theoretical and practical aspects of multimodel query processing techniques to identify the directions for the development of heterogeneous data management systems. The article presents an algorithm for multimodel query processing, covering stages from loading data schemas and query formulation to result aggregation and presentation. A comparative analysis was conducted to evaluate the alignment of three theoretical and practical approaches for constructing a unified data model with the proposed algorithm: set-theoretic object-oriented description, lambda calculus-based methods, and category theory. The results demonstrate that all approaches provide a rigorously developed framework for a truly universal data model, accommodating individual model features and enabling integrated multimodel data processing. However, most approaches focus exclusively on data retrieval operations and lack comprehensive cost estimation models for query execution planning. Consequently, key research directions are identified: supporting the full set of DDL and DML operations and developing methodologies for evaluation of query efficiency based on execution costs, operation complexity, query execution statistics, and multimodel data characteristics.
How to Cite:
Volkov A.S., Vinogradova M.V. and Afanasyev G.I. Comparative analysis of theoretical and practical approaches to multi-model query processing: Current limitations and research directions. Computational Nanotechnology. 13, 2 (2026), 34–50. DOI: 10.33693/2313-223X-2026-13-2-34-50. EDN: ZNPGWW
Reference list:
Bashkin V.A. Lambda Calculus: study guide. Yaroslavl: Yaroslavl State University named after P.G. Demidov (YarSU), 2018. 52 p.
Volkov A.S., Vinogradova M.V. Application of a unified data model for solving the problem of polyglot persistence. In: Artificial intelligence in management, control, and data processing systems (IIASU’24). Proceedings of the III All-Russian scientific conference. In 3 vols. Moscow: KDU. 2025. Vol. 1. Pp. 423-431. EDN YEEOTL.
Voronov M.V. Modeling of weakly structured problems. Monograph. Moscow: Publishing House of the Modern University for the Humanities, 2010. 332 p. ISBN: 978-5-8323-0702-2. EDN: UDYLLF.
Date C.J. Introduction to database systems. Transl. from English. K.A. Ptitsyn (ed.). 8th ed. Moscow: Williams, 2018. 1327 p. ISBN: 978-5-8459-0788-2. EDN: QMSTGX.
MacLane S. Categories for the working mathematician. V.A. Artamonov (transl. ed.). Moscow: Fizmatlit, 2004. 351 p. ISBN: 5-9221-0400-4. EDN: QJMIXL.
Novikov B.A., Gorshkova E.A., Grafeeva N.G. Fundamentals of database technologies. Textbook. E.V. Rogov (ed.). 2nd ed. Moscow: DMK Press, 2020. 582 p. ISBN: 978-5-97060-841-8.
Azeroual O., Nikiforova A., Sha K. Overlooked aspects of data governance: Workflow framework for enterprise data deduplication. In: International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS). 2023. Pp. 65–73. DOI: 10.1109/ICCNS58795.2023.10193478.
Candel C.J.F., García-Molina J.J., Ruiz D.S. A unified metamodel for NoSQL and relational databases. Information Systems. 2022. Vol. 104 (2022). Art. 101898. Pp. 1–26. DOI: 10.1016/j.is.2021.101898.
Fernández Candel J., García-Molina J.J, Sevilla Ruiz D. SkiQL: A unified schema query language. Data & Knowledge Engineering. 2023. Vol. 148 (2023). Art. 102234. Pp. 1–20. DOI: 10.1016/j.datak.2023.102234.
Gog I., Schwarzkopf M., Crooks N. et al. Musketeer: All for one, one for all in data processing systems. In: Proceedings of the 10th European Conference on Computer Systems (EuroSys 2015). No. 2. Pp. 1–16. DOI: 10.1145/2741948.2741968.
Guo Q., Zhang C., Zhang S., Lu J. Multi-model query languages: Taming the variety of big data. Distributed and Parallel Databases. 2024. Vol. 42. No. 1, Pp. 31–71. DOI: 10.1007/s10619-023-07433-1.
Janković S., Mladenović S., Mladenović D. et al. Schema on read modeling approach as a basis of big data analytics integration in EIS. Enterprise Information Systems. 2018. Vol. 12. No. 8-9. Pp. 1180–1201. DOI: 10.1080/17517575.2018.1462404.
Pwint Phyu Khine, Zhaoshun Wang. A Review of polyglot persistence in the big data world. Information. 2019. Vol. 10. No. 4. Art. 141. Pp. 1–24. DOI: 10.3390/info10040141.
Koupil P., Crha D., Holubová I. A universal approach for simplified redundancy-aware cross-model querying. Information Systems. 2025. Vol. 127. Art. 102456. DOI: 10.1016/j.is.2024.102456.
Koupil P., Crha D., Holubová I. MM-quecat: A tool for unified querying of multi-model data. In: Proceedings of the 26th International Conference on Extending Database Technology (EDBT). 2023. Pp. 831–834. DOI: 10.48786/edbt.2023.76.
Koupil P., Holubová I. A unified representation and transformation of multi-model data using category theory. Journal of Big Data. 2022. Vol. 9. No. 61 (2022). Pp. 1–49. DOI: 10.1186/s40537-022-00613-3.
Koupil P., Holubová I. Unifying categorical representation of multi-model data. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 2022. Pp. 365–371. DOI: 10.1145/3477314.3507690.
Koupil P., Svoboda M., Holubová I. MM-cat: A tool for modeling and transformation of multi-model data using category theory. In: Proceedings of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 2021. Pp. 635–639. DOI: 10.1109/MODELS-C53483.2021.00098.
Macak M., Stovcik M., Buhnova B., Merjavy M. How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB. In: Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS). Sofia, Bulgaria, 2020. Pp. 463–470. DOI: 10.15439/2020F76.
Pokorný J. Data integration in a multi-model environment. In: Information Integration and Web Intelligence (iiWAS). 2023. Pp. 121–127. DOI: 10.1007/978-3-031-48316-5_14.
Pokorný J. Integration of relational and graph databases functionally. Foundations of Computing and Decision Sciences. Vol. 44 (2019). No. 4. Pp. 427–441. DOI: 10.2478/fcds-2019-0021.
Pokorný J. Integration of relational and NoSQL databases. Intelligent Information and Database Systems, ACIIDS 2018. Lecture Notes in Computer Science. Vol. 10752. Pp. 35–45. Cham: Springer. 2018.
Pokorný J. JSON Functionally. In: Advances in Databases and Information Systems. Cham: Springer, 2020. Pp. 139–153. DOI:10.1007/978-3-030-54832-2_12.
Pokorný J., Richta K. Towards conceptual and logical modelling of NoSQL databases. In: Advances in Information Systems Development. Cham: Springer, 2022. Pp. 255–272. DOI: 10.1007/978-3-030-95354-6_15.
Sadalage P.J., Fowler M. NoSQL distilled: A brief guide to the emerging world of polyglot persistence. Addison-Wesley Professional. 2012. ISBN: 978-0-13-303612-1.
Ye F., Sheng X., Nedjah N. et al. A benchmark for performance evaluation of a multi-model database vs. polyglot persistence. Journal of Database Management (JDM). 2023. Vol. 34. No. 3. P. 1–20. DOI: 10.4018/JDM.321756.
Volkov A.S., Vinogradova M.V. Application of a unified data model for solving the problem of polyglot persistence. In: Artificial intelligence in management, control, and data processing systems (IIASU’24). Proceedings of the III All-Russian scientific conference. In 3 vols. Moscow: KDU. 2025. Vol. 1. Pp. 423-431. EDN YEEOTL.
Voronov M.V. Modeling of weakly structured problems. Monograph. Moscow: Publishing House of the Modern University for the Humanities, 2010. 332 p. ISBN: 978-5-8323-0702-2. EDN: UDYLLF.
Date C.J. Introduction to database systems. Transl. from English. K.A. Ptitsyn (ed.). 8th ed. Moscow: Williams, 2018. 1327 p. ISBN: 978-5-8459-0788-2. EDN: QMSTGX.
MacLane S. Categories for the working mathematician. V.A. Artamonov (transl. ed.). Moscow: Fizmatlit, 2004. 351 p. ISBN: 5-9221-0400-4. EDN: QJMIXL.
Novikov B.A., Gorshkova E.A., Grafeeva N.G. Fundamentals of database technologies. Textbook. E.V. Rogov (ed.). 2nd ed. Moscow: DMK Press, 2020. 582 p. ISBN: 978-5-97060-841-8.
Azeroual O., Nikiforova A., Sha K. Overlooked aspects of data governance: Workflow framework for enterprise data deduplication. In: International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS). 2023. Pp. 65–73. DOI: 10.1109/ICCNS58795.2023.10193478.
Candel C.J.F., García-Molina J.J., Ruiz D.S. A unified metamodel for NoSQL and relational databases. Information Systems. 2022. Vol. 104 (2022). Art. 101898. Pp. 1–26. DOI: 10.1016/j.is.2021.101898.
Fernández Candel J., García-Molina J.J, Sevilla Ruiz D. SkiQL: A unified schema query language. Data & Knowledge Engineering. 2023. Vol. 148 (2023). Art. 102234. Pp. 1–20. DOI: 10.1016/j.datak.2023.102234.
Gog I., Schwarzkopf M., Crooks N. et al. Musketeer: All for one, one for all in data processing systems. In: Proceedings of the 10th European Conference on Computer Systems (EuroSys 2015). No. 2. Pp. 1–16. DOI: 10.1145/2741948.2741968.
Guo Q., Zhang C., Zhang S., Lu J. Multi-model query languages: Taming the variety of big data. Distributed and Parallel Databases. 2024. Vol. 42. No. 1, Pp. 31–71. DOI: 10.1007/s10619-023-07433-1.
Janković S., Mladenović S., Mladenović D. et al. Schema on read modeling approach as a basis of big data analytics integration in EIS. Enterprise Information Systems. 2018. Vol. 12. No. 8-9. Pp. 1180–1201. DOI: 10.1080/17517575.2018.1462404.
Pwint Phyu Khine, Zhaoshun Wang. A Review of polyglot persistence in the big data world. Information. 2019. Vol. 10. No. 4. Art. 141. Pp. 1–24. DOI: 10.3390/info10040141.
Koupil P., Crha D., Holubová I. A universal approach for simplified redundancy-aware cross-model querying. Information Systems. 2025. Vol. 127. Art. 102456. DOI: 10.1016/j.is.2024.102456.
Koupil P., Crha D., Holubová I. MM-quecat: A tool for unified querying of multi-model data. In: Proceedings of the 26th International Conference on Extending Database Technology (EDBT). 2023. Pp. 831–834. DOI: 10.48786/edbt.2023.76.
Koupil P., Holubová I. A unified representation and transformation of multi-model data using category theory. Journal of Big Data. 2022. Vol. 9. No. 61 (2022). Pp. 1–49. DOI: 10.1186/s40537-022-00613-3.
Koupil P., Holubová I. Unifying categorical representation of multi-model data. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 2022. Pp. 365–371. DOI: 10.1145/3477314.3507690.
Koupil P., Svoboda M., Holubová I. MM-cat: A tool for modeling and transformation of multi-model data using category theory. In: Proceedings of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 2021. Pp. 635–639. DOI: 10.1109/MODELS-C53483.2021.00098.
Macak M., Stovcik M., Buhnova B., Merjavy M. How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB. In: Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS). Sofia, Bulgaria, 2020. Pp. 463–470. DOI: 10.15439/2020F76.
Pokorný J. Data integration in a multi-model environment. In: Information Integration and Web Intelligence (iiWAS). 2023. Pp. 121–127. DOI: 10.1007/978-3-031-48316-5_14.
Pokorný J. Integration of relational and graph databases functionally. Foundations of Computing and Decision Sciences. Vol. 44 (2019). No. 4. Pp. 427–441. DOI: 10.2478/fcds-2019-0021.
Pokorný J. Integration of relational and NoSQL databases. Intelligent Information and Database Systems, ACIIDS 2018. Lecture Notes in Computer Science. Vol. 10752. Pp. 35–45. Cham: Springer. 2018.
Pokorný J. JSON Functionally. In: Advances in Databases and Information Systems. Cham: Springer, 2020. Pp. 139–153. DOI:10.1007/978-3-030-54832-2_12.
Pokorný J., Richta K. Towards conceptual and logical modelling of NoSQL databases. In: Advances in Information Systems Development. Cham: Springer, 2022. Pp. 255–272. DOI: 10.1007/978-3-030-95354-6_15.
Sadalage P.J., Fowler M. NoSQL distilled: A brief guide to the emerging world of polyglot persistence. Addison-Wesley Professional. 2012. ISBN: 978-0-13-303612-1.
Ye F., Sheng X., Nedjah N. et al. A benchmark for performance evaluation of a multi-model database vs. polyglot persistence. Journal of Database Management (JDM). 2023. Vol. 34. No. 3. P. 1–20. DOI: 10.4018/JDM.321756.
Keywords:
multimodel queries, algorithm, DBMS, unified model, category theory, lambda calculus.
