A Study on Polyglot Data Modeling
Abstract
Polyglot persistence is seen as the future of database modeling, as it aims to adapt each part of a conceptual database modeling to logical and physical schemas with the best possible performance in terms of storage and access. However, polyglot data modeling brings new challenges to the designer, such as dealing with more than one database technology and choosing the best logical model or database technology to maintain and manage a certain part of a conceptual modeling. This article presents a systematic review of the literature on this research area, an overview of the found works and a comparative analysis of them. We did not find a study similar to this in the literature.
References
Fowler, M. (2011). Polyglot persistence. [link]. Last access: 19 February 2024.
Holubová, I., Vavrek, M., and Scherzinger, S. (2021). Evolution management in multi-model databases. Data & Knowledge Engineering, 136:101932.
Khine, P. P. and Wang, Z. (2019). A Review of Polyglot Persistence in the Big Data World. Information, 10(4):141.
Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University, UK, 33(2004):1–26.
Kolonko, M. and Müllenbach, S. (2020). Polyglot Persistence in Conceptual Modeling for Information Analysis. In 10th International Conference on Advanced Computer Information Technologies (ACIT), pages 590–594. IEEE.
Koupil, P. and Holubová, I. (2022). A Unified Representation and Transformation of Multi-model Data using Category Theory. Journal of Big Data, 9(1):61.
Koupil, P., Hricko, S., and Holubová, I. (2022). A universal approach for multi-model schema inference. Journal of Big Data, 9(1):1–46.
Koupil, P., Svoboda, M., and Holubová, I. (2021). Mm-cat: A tool for modeling and transformation of multi-model data using category theory. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 635–639.
Lu, J. and Holubová, I. (2017). Multi-model data management: What’s new and what’s next? In Proceeding of the 20th international conference on extended databases.
Mac Lane, S. (2013). Categories for the working mathematician, volume 5. Springer Science & Business Media.
Niska, P. (2024). Multi-model Database Migration. PhD thesis, University of Helsinki, Faculty of Science.
Roy-Hubara, N., Shoval, P., and Sturm, A. (2022). Selecting Databases for Polyglot Persistence Applications. Data Knowl. Eng., 137(C).
Sadalage, P. J. and Fowler, M. (2013). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Pearson Education.
Schreiner, G. A., Duarte, D., and Mello, R. d. S. (2019). When Relational-based Applications Go to NoSQL Databases: A Survey. Information, 10(7):241.
Stonebraker, M. (2012). New Opportunities for NewSQL. Communications of the ACM, 55:10–11.
Vavrek, M., Holubová, I., and Scherzinger, S. (2019). Mm-evolver: A multi-model evolution management tool. In EDBT, volume 19, pages 586–589.
Wazlawick, R. S. (2009). Metodologia de Pesquisa para Ciência da Computação, volume 2. Elsevier.
Ye, F., Sheng, X., Nedjah, N., Sun, J., and Zhang, P. (2023). A benchmark for performance evaluation of a multi-model database vs. polyglot persistence. Journal of Database Management (JDM), 34(3):1–20.
