大数据时代，数据规模庞大、数据管理应用场景复杂，传统数据库和数据管理技术面临很大的挑战.人工智能技术因其强大的学习、推理、规划能力，为数据库系统提供了新的发展机遇.人工智能赋能的数据库系统通过对数据分布、查询负载、性能表现等特征进行建模和学习，自动地进行查询负载预测、数据库配置参数调优、数据分区、索引维护、查询优化、查询调度等，以不断提高数据库针对特定硬件、数据和负载的性能.同时，一些机器学习模型可以替代数据库系统中的部分组件，有效减少开销，如学习型索引结构等.分析了人工智能赋能的数据管理新技术的研究进展，总结了现有方法的问题和解决思路，并对未来研究方向进行了展望.

Traditional database systems and data management techniques are facing great challenge due to the 3V's in big data. The development of artificial intelligence provides a brand-new opportunity for database management systems with its power in learning, reasoning, and planning. Through learning from data distribution, query workload and query execution performance, the systems powered by artificial intelligence are able to forecast future workload, tune database configurations, partition data blocks, index on proper columns, estimate selectivity, optimize query plan and control query concurrency automatically. Also, some machine learning models can replace core components of a database such as index structures. This study introduces new research on database systems with artificial intelligence and state the existing problems and potential solutions and the future research directions are proposed as well.

国家重点研发计划（2018YFB1004401）；国家自然科学基金（61772537，61772536，61702522，61532021）