NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
<|reference_start|>NeurDB: On the Design and Implementation of an AI-powered Autonomous Database: 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.<|reference_end|>
@article{zhao2024neurdb:,
    title={NeurDB: On the Design and Implementation of an AI-powered Autonomous
  Database},
    author={Zhanhao Zhao, Shaofeng Cai, Haotian Gao, Hexiang Pan, Siqi Xiang,
  Naili Xing, Gang Chen, Beng Chin Ooi, Yanyan Shen, Yuncheng Wu, Meihui Zhang},
    journal={arXiv preprint arXiv:2408.03013},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03013},
    primaryClass={cs.DB cs.AI cs.LG}
}