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LANTERN: Boredom-conscious Natural Language Description Generation of Query Execution Plans for Database Education

Proceedings. ACM-Sigmod International Conference on Management of Data(2022)

Xidian Univ | Nanyang Technol Univ

Cited 11|Views29
Abstract
The database systems course in an undergraduate computer science degree program is gaining increasing importance due to the continuous supply of database-related jobs as well as the rise of Data Science. A key learning goal of learners taking such a course is to understand how SQL queries are executed in an RDBMS in practice. An RDBMS typically exposes a query execution plan (QEP) in a visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a learner to comprehend these QEPs containing vendor-specific implementation details. In this demonstration, we present a novel, generic, and portable system called LANTERN that generates a natural language (NL)-based description of the execution strategy chosen by the underlying RDBMS to process a query. It provides a declarative framework called POOL for subject matter experts (SME) to efficiently create and manipulate the NL descriptions of physical operators of any RDBMS. It then exploits POOL to generate the NL descriptions of QEPs by integrating a rule-based and a deep learning-based techniques to infuse language variability in the descriptions. Such an NL generation strategy mitigates the impact of boredom on learners caused by repeated exposure of similar text generated by a rule-based system.
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database education,query execution plan,natural language generation
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要点】:论文提出了一种名为LANTERN的系统,通过生成自然语言描述的查询执行计划,帮助数据库课程学习者更好地理解SQL查询在关系数据库管理系统(RDBMS)中的执行过程,创新点在于采用规则和深度学习相结合的方法增加描述的语言多样性,减少学习者的枯燥感。

方法】:LANTERN使用一个声明式框架POOL,允许领域专家高效创建和操作任意RDBMS物理操作的自然语言描述,并结合规则基础和深度学习技术生成查询执行计划的NL描述。

实验】:论文中未具体描述实验细节和数据集名称,但提到通过使用规则和深度学习技术,LANTERN能够生成具有语言多样性的查询执行计划描述,从而提高学习者的理解和参与度。