| 引用本文: | 姬涛,钟锴,李奕言,李翠平,陈红.AI赋能的关系型数据库系统研究: 标准化、技术与挑战.软件学报,2026,37(2):817-859 |
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| AI赋能的关系型数据库系统研究: 标准化、技术与挑战 |
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姬涛1,2, 钟锴1,2, 李奕言1,2, 李翠平1,2, 陈红1,2
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1.中国人民大学 信息学院, 北京 100872;2.数据工程与知识工程教育部重点实验室 (中国人民大学), 北京 100872
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| 摘要: |
| 随着大数据时代的到来, 海量数据应用呈现出规模性(volume)、多样性(variety)、高速性(velocity)和价值性(value)的典型特征. 这种数据范式对传统数据采集方法、管理策略及数据库处理能力提出了革命性挑战. 近年来, 人工智能技术的突破性发展, 特别是机器学习和深度学习在表征学习能力、计算效率提升及模型可解释性方面的显著进步, 为应对这些挑战提供了创新性解决方案. 在此背景下, 人工智能与数据库系统的深度融合催生了新一代智能数据库管理系统. 这类系统通过AI技术深度赋能实现了交互层、管理层、内核层这3大核心创新: 面向终端用户的自然语言交互; 支持自动化运维的数据库管理框架(如参数调优、索引推荐、数据库诊断和负载管理等); 基于机器学习的高效可扩展内核组件(如学习索引、智能分区、智能查询优化、智能查询调度等). 此外, 新兴的智能组件开发接口(API)进一步降低了AI与数据库系统的集成门槛. 系统性地探讨智能数据库的关键问题, 以“标准化”为核心视角, 提炼出各研究主题(交互范式、管理架构和内核设计)内在的通用处理范式和特征. 通过深入分析这些标准化的流程、组件接口与协作机制, 揭示驱动智能数据库自优化的核心逻辑, 综述当前研究进展, 并深入分析该领域面临的技术挑战与未来发展方向. |
| 关键词: 数据库系统 数据管理 人工智能 机器学习 |
| DOI：10.13328/j.cnki.jos.007506 |
| 分类号:TP311 |
| 基金项目:国家重点研发计划(2023YFB4503600); 国家自然科学基金(U23A20299, U24B20144, 62172424, 62276270, 62322214) |
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| Empowering Relational Database Systems with AI: Standardization, Technologies, and Challenges |
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JI Tao1,2, ZHONG Kai1,2, LI Yi-Yan1,2, LI Cui-Ping1,2, CHEN Hong1,2
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1.School of Information, Renmin University of China, Beijing 100872, China;2.Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China
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| Abstract: |
| The advent of the big data era has introduced massive data applications characterized by four defining attributes: volume, variety, velocity, and value. These attributes pose revolutionary challenges to conventional data acquisition methods, management strategies, and database processing capabilities. Recent breakthroughs in artificial intelligence (AI), particularly in machine learning and deep learning, have demonstrated remarkable advancements in representation learning, computational efficiency, and model interpretability, thus offering innovative solutions to these challenges. This convergence of AI and database systems has given rise to a new generation of intelligent database management systems, which integrate AI technologies across three core architectural layers: (1) natural language interfaces for user interaction, (2) automated database administration frameworks (including parameter tuning, index recommendation, database diagnostics, and workload management), and (3) machine learning-based efficient and scalable components (such as learned indexes, adaptive partitioning, query optimization, and scheduling). Furthermore, new intelligent component application programming interfaces (APIs) have lowered the integration barrier between AI and database systems. This study systematically investigates intelligent databases through a standardization-centric framework, delineating common processing paradigms across the research themes of interaction paradigms, management architectures, and kernel design. By examining standardized processes, interfaces, and collaboration mechanisms, this study uncovers the core logic enabling database self-optimization, reviews current research advancements, and provides an in-depth analysis of the technical challenges and prospects for future development. |
| Key words: database system data management artificial intelligence (AI) machine learning |
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