With the continuous development of NoSQL databases, more and more developers choose to use semi-structured data for development and data management, which puts forward requirements for schema management of semi-structured data stored in NoSQL databases. Schema extraction plays an important role in understanding schemas, optimizing queries, and validating data consistency. Therefore, in this survey we investigate structural methods based on tree and graph and statistical methods based on distributed architecture and machine learning to extract schemas. The schemas obtained by the structural methods are more interpretable, and the statistical methods have better applicability and generalization ability. Moreover, we also investigate tools and systems for schemas extraction. Schema extraction tools are mainly used for spark or NoSQL databases, and are suitable for small datasets or simple application environments. The system mainly focuses on the extraction and management of schemas in large data sets and complex application scenarios. Furthermore, we also compare these techniques to facilitate data managers' choice.

翻译：随着NOSQL数据库的持续开发,越来越多的开发商选择使用半结构化数据进行开发和数据管理,这就提出了对储存在NOSQL数据库中的半结构化数据进行系统管理的要求。Schema提取在理解系统图、优化查询和验证数据一致性方面发挥着重要作用。因此,在本次调查中,我们根据分布式结构和机器学习提取系统图和统计方法,调查基于树图的结构方法,通过分布式结构和机器学习提取系统图的统计方法。通过结构方法获得的系统图比较容易解释,而统计方法具有更好的适用性和一般化能力。此外,我们还调查了化学提取工具和系统。Schema提取工具主要用于火花或 NoSQL数据库,适合小型数据集或简单应用环境。该系统主要侧重于大型数据集和复杂应用情景中的系统图集的提取和管理。此外,我们还比较这些技术以便利数据管理员的选择。