A Formal Category Theoretical Framework for Multi-model Data Transformations
| Autor: | Uotila, Valter, Lu, Jiaheng |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Rezig E.K. et al. (eds) Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH 2021, Poly 2021. Lecture Notes in Computer Science, vol 12921. Pages 14-28. Springer, Cham |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/978-3-030-93663-1_2 |
| Popis: | Data integration and migration processes in polystores and multi-model database management systems highly benefit from data and schema transformations. Rigorous modeling of transformations is a complex problem. The data and schema transformation field is scattered with multiple different transformation frameworks, tools, and mappings. These are usually domain-specific and lack solid theoretical foundations. Our first goal is to define category theoretical foundations for relational, graph, and hierarchical data models and instances. Each data instance is represented as a category theoretical mapping called a functor. We formalize data and schema transformations as Kan lifts utilizing the functorial representation for the instances. A Kan lift is a category theoretical construction consisting of two mappings satisfying a certain universal property. In this work, the two mappings correspond to schema transformation and data transformation. Comment: 15 pages, 4 figures, Heterogeneous Data Management, Polystores, and Analytics for Healthcare, VLDB Workshops, Poly 2021 and DMAH 2021 |
| Databáze: | arXiv |
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