论文标题
半结构化数据的架构提取
Schema Extraction on Semi-structured Data
论文作者
论文摘要
随着NOSQL数据库的持续开发，越来越多的开发人员选择将半结构化数据用于开发和数据管理，这为NOSQL数据库中存储的半结构化数据的架构管理提供了要求。模式提取在理解模式，优化查询和验证数据一致性方面起着重要作用。因此，在本调查中，我们根据树木和图和统计方法研究了基于分布式体系结构和机器学习以提取模式的结构方法。通过结构方法获得的模式更可解释，统计方法具有更好的适用性和泛化能力。此外，我们还研究了用于提取方案的工具和系统。架构提取工具主要用于SPARK或NOSQL数据库，适用于小型数据集或简单的应用程序环境。该系统主要关注大型数据集和复杂应用程序方案中的模式的提取和管理。此外，我们还比较了这些技术，以促进数据经理的选择。
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.
