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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.10546 |
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| _version_ | 1866915063587143680 |
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| author | Alabi, Daniel Wu, Eugene |
| author_facet | Alabi, Daniel Wu, Eugene |
| contents | The emerging discipline of Computational Science is concerned with using computers to simulate or solve scientific problems. These problems span the natural, political, and social sciences. The discipline has exploded over the past decade due to the emergence of larger amounts of observational data and large-scale simulations that were previously unavailable or unfeasible. However, there are still significant challenges with managing the large amounts of data and simulations.
The database management systems community has always been at the forefront of the development of the theory and practice of techniques for formalizing and actualizing systems that access or query large datasets. In this paper, we present EmpireDB, a vision for a data management system to accelerate computational sciences. In addition, we identify challenges and opportunities for the database community to further the fledgling field of computational sciences. Finally, we present preliminary evidence showing that the optimized components in EmpireDB could lead to improvements in performance compared to contemporary implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EmpireDB: Data System to Accelerate Computational Sciences Alabi, Daniel Wu, Eugene Databases The emerging discipline of Computational Science is concerned with using computers to simulate or solve scientific problems. These problems span the natural, political, and social sciences. The discipline has exploded over the past decade due to the emergence of larger amounts of observational data and large-scale simulations that were previously unavailable or unfeasible. However, there are still significant challenges with managing the large amounts of data and simulations. The database management systems community has always been at the forefront of the development of the theory and practice of techniques for formalizing and actualizing systems that access or query large datasets. In this paper, we present EmpireDB, a vision for a data management system to accelerate computational sciences. In addition, we identify challenges and opportunities for the database community to further the fledgling field of computational sciences. Finally, we present preliminary evidence showing that the optimized components in EmpireDB could lead to improvements in performance compared to contemporary implementations. |
| title | EmpireDB: Data System to Accelerate Computational Sciences |
| topic | Databases |
| url | https://arxiv.org/abs/2412.10546 |