| 摘要: |
| 应用驱动创新，数据库技术就是在支持主流应用的提质降本增效中发展起来的.从OLTP、OLAP到今天的在线机器学习建模无不如此.机器学习是当前人工智能技术落地的主要途径，通过对数据进行建模而提取知识、实现预测分析.从数据管理的视角对机器学习训练过程进行解构和建模，从数据选择、数据存储、数据存取、自动优化和系统实现等方面，综述了数据管理技术的应用及优缺点，在此基础上，提出支持在线机器学习的数据管理技术的若干关键技术挑战. |
| 关键词: 人工智能 机器学习 数据管理 |
| DOI：10.13328/j.cnki.jos.006182 |
| 分类号: |
| 基金项目:国家自然科学基金（62072458） |
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| Survey on Data Management Technology for Machine Learning |
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CUI Jian-Wei1,2, ZHAO Zhe1,2, DU Xiao-Yong1,2
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1.Key Laboratory of Data Engineering and Knowledge Engineering, MOE(Renmin University of China), Beijing 100872, China;2.School of Information, Renmin University of China, Beijing 100872, China
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| Abstract: |
| Applications drive innovation. The advance of database technology is achieved in support of development of mainstream applications effectively and efficiently. OLTP, OLAP, and online machine learning modeling today all follow this trend. Machine learning extracts knowledge and realizes predictive analysis by modeling data, is the main approach of artificial intelligence technology. This work studies the training process of machine learning from the perspective of data management, summarizes data management technology through data selection, data storage, data access, automatic optimization, and system implementation, and analyzes the advantages and disadvantages of these techniques. Based on the analysis, this study proposes key challenges of data management technology for online machine learning. |
| Key words: artificial intelligence machine learning data management |
