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首页 > 过刊浏览>2020年第31卷第3期 >600-619. DOI:10.13328/j.cnki.jos.005909
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人工智能赋能的数据管理技术研究
作者:
作者简介:

孙路明(1994-),男,山东烟台人,博士生,主要研究领域为数据库系统,机器学习;李翠平(1971-),女,博士,教授,博士生导师,CCF杰出会员,主要研究领域为社交网络分析,社会推荐,大数据分析及挖掘;张少敏(1996-),男,硕士生,主要研究领域为索引技术,机器学习;陈红(1965-),女,博士,教授,博士生导师,CCF杰出会员,主要研究领域为数据库技术,新硬件平台下的高性能计算;姬涛(1997-),男,硕士生,主要研究领域为查询优化,机器学习.

通讯作者:

李翠平,E-mail:licuiping@ruc.edu.cn

基金项目:

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


Survey of Data Management Techniques Powered by Artificial Intelligence
Author:
Fund Project:

National Key Research and Development Program of China (2018YFB1004401); National Natural Science Foundation of China (61772537, 61772536, 61702522, 61532021)

  • 摘要
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    摘要:

    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.

    Abstract:

    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.

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孙路明,张少敏,姬涛,李翠平,陈红.人工智能赋能的数据管理技术研究.软件学报,2020,31(3):600-619

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  • 收稿日期:2019-07-20
  • 最后修改日期:2019-09-10
  • 在线发布日期: 2019-12-06
  • 出版日期: 2020-03-06
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