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Reversible Mapping of Relational and Graph Databases
| Author(s): | Palagashvili A. M., Stupnikov S. A. |
| Published: | Pattern Recognition and Image Analysis, 2023. Vol. 33. Iss. 2. P. 113–121. |
| Abstract: | |
| In the contemporary world, a large amount of heterogeneous data are accumulated, which have different nature and require specific approaches to their processing and storage. Even within one information system, it is often required to process data represented in different data models from the same knowledge domain. One way to solve this problem is multimodel databases, which simultaneously support several data models. These database management systems generally imply the division into “primary” and “secondary” data models, as well as require explicit mapping of data schemas. The relational data model appeared a long time ago; it is well studied and widely used. On the other hand, graph data models, which are suitable for social networks, recommender services, transport networks, etc., are become increasingly popular. In this paper, we propose algorithms for mapping relational and graph databases the composition of which is an identity mapping. These algorithms form a basis for creating multimodel graph-relational database management systems.
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| Download: |
[ https://link.springer.com/article/10.1134/S1054661823020098 ]
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