SchemaDB: Structures in Relational Datasets
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913864487010304 |
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| author | Christopher, Cody James Moore, Kristen Liebowitz, David |
| author_facet | Christopher, Cody James Moore, Kristen Liebowitz, David |
| contents | In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats. Databases are not commonly shared publicly for reasons of privacy and security, so schemata are not available for study. Consequently, an understanding of database structures in the wild is lacking, and most examples found publicly belong to common development frameworks or are derived from textbooks or engine benchmark designs. SchemaDB contains 2,500 samples of relational schemata found in public repositories which we have standardised to MySQL syntax. We provide our gathering and transformation methodology, summary statistics, and structural analysis, and discuss potential downstream research tasks in several domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_12835 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | SchemaDB: Structures in Relational Datasets Christopher, Cody James Moore, Kristen Liebowitz, David Databases Machine Learning In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats. Databases are not commonly shared publicly for reasons of privacy and security, so schemata are not available for study. Consequently, an understanding of database structures in the wild is lacking, and most examples found publicly belong to common development frameworks or are derived from textbooks or engine benchmark designs. SchemaDB contains 2,500 samples of relational schemata found in public repositories which we have standardised to MySQL syntax. We provide our gathering and transformation methodology, summary statistics, and structural analysis, and discuss potential downstream research tasks in several domains. |
| title | SchemaDB: Structures in Relational Datasets |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2111.12835 |