NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929659040497664 |
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| author | Zhao, Zhanhao Cai, Shaofeng Gao, Haotian Pan, Hexiang Xiang, Siqi Xing, Naili Chen, Gang Ooi, Beng Chin Shen, Yanyan Wu, Yuncheng Zhang, Meihui |
| author_facet | Zhao, Zhanhao Cai, Shaofeng Gao, Haotian Pan, Hexiang Xiang, Siqi Xing, Naili Chen, Gang Ooi, Beng Chin Shen, Yanyan Wu, Yuncheng Zhang, Meihui |
| contents | Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the dynamic nature of databases, which renders them ineffective for real-world applications characterized by evolving data and workloads. This paper introduces NeurDB, an AI-powered autonomous database that deepens the fusion of AI and databases with adaptability to data and workload drift. NeurDB establishes a new in-database AI ecosystem that seamlessly integrates AI workflows within the database. This integration enables efficient and effective in-database AI analytics and fast-adaptive learned system components. Empirical evaluations demonstrate that NeurDB substantially outperforms existing solutions in managing AI analytics tasks, with the proposed learned components more effectively handling environmental dynamism than state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeurDB: On the Design and Implementation of an AI-powered Autonomous Database Zhao, Zhanhao Cai, Shaofeng Gao, Haotian Pan, Hexiang Xiang, Siqi Xing, Naili Chen, Gang Ooi, Beng Chin Shen, Yanyan Wu, Yuncheng Zhang, Meihui Databases Artificial Intelligence Machine Learning Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the dynamic nature of databases, which renders them ineffective for real-world applications characterized by evolving data and workloads. This paper introduces NeurDB, an AI-powered autonomous database that deepens the fusion of AI and databases with adaptability to data and workload drift. NeurDB establishes a new in-database AI ecosystem that seamlessly integrates AI workflows within the database. This integration enables efficient and effective in-database AI analytics and fast-adaptive learned system components. Empirical evaluations demonstrate that NeurDB substantially outperforms existing solutions in managing AI analytics tasks, with the proposed learned components more effectively handling environmental dynamism than state-of-the-art approaches. |
| title | NeurDB: On the Design and Implementation of an AI-powered Autonomous Database |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.03013 |