NeurDB：论AI驱动的自治数据库的设计与实现
NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
AI总结：
本文提出AI驱动的自治数据库NeurDB，通过在数据库内无缝集成AI工作流，支持库内分析与快速自适应的学习型系统组件，以应对数据和工作负载动态变化。
摘要：
数据库正越来越多地采用AI来提供自治系统优化和智能库内分析，旨在减轻各行业终端用户的负担。然而，现有大多数方法未能考虑数据库的动态特性，这使其在以数据和工作负载不断演化为特征的实际应用中效果不佳。本文介绍NeurDB，这是一个AI驱动的自治数据库，深化了AI与数据库的融合，并能够适应数据和工作负载漂移。NeurDB建立了全新的库内AI生态系统，将AI工作流无缝集成到数据库内部。这种集成支持高效且有效的库内AI分析，以及快速自适应的学习型系统组件。实证评估表明，NeurDB在管理AI分析任务方面显著优于现有解决方案，并且所提出的学习型组件在处理环境动态性时比最先进方法更有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
