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arXiv · 2408.03013

NeurDB: On the Design and Implementation of an AI-powered Autonomous Database

Zhanhao Zhao·Shaofeng Cai·Haotian Gao·Hexiang Pan·Siqi Xiang·Naili Xing·Gang Chen·Beng Chin Ooi·Yanyan Shen·Yuncheng Wu·Meihui Zhang
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Abstract

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.

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Zhanhao Zhao, Shaofeng Cai, Haotian Gao, Hexiang Pan, Siqi Xiang, Naili Xing, Gang Chen, Beng Chin Ooi, Yanyan Shen, Yuncheng Wu, Meihui Zhang. 2024-08-06. NeurDB: On the Design and Implementation of an AI-powered Autonomous Database. https://arxiv.org/abs/2408.03013

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