NeurDB: An AI-powered Autonomous Data System
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912026323845120 |
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| author | Ooi, Beng Chin Cai, Shaofeng Chen, Gang Shen, Yanyan Tan, Kian-Lee Wu, Yuncheng Xiao, Xiaokui Xing, Naili Yue, Cong Zeng, Lingze Zhang, Meihui Zhao, Zhanhao |
| author_facet | Ooi, Beng Chin Cai, Shaofeng Chen, Gang Shen, Yanyan Tan, Kian-Lee Wu, Yuncheng Xiao, Xiaokui Xing, Naili Yue, Cong Zeng, Lingze Zhang, Meihui Zhao, Zhanhao |
| contents | In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03924 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeurDB: An AI-powered Autonomous Data System Ooi, Beng Chin Cai, Shaofeng Chen, Gang Shen, Yanyan Tan, Kian-Lee Wu, Yuncheng Xiao, Xiaokui Xing, Naili Yue, Cong Zeng, Lingze Zhang, Meihui Zhao, Zhanhao Databases Artificial Intelligence Machine Learning In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan. |
| title | NeurDB: An AI-powered Autonomous Data System |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.03924 |