The widespread usage of rdbms in the commercial world has played a pivotal role in the offering of database systems course in major universities. A key challenge encountered by learners taking such a course is the topic of query optimization. The query optimization process produces a query execution plan (qep), which represents an execution strategy for an sql query. Unfortunately, in practice, it is often difficult for a learner to comprehend query execution strategies by perusing vendor-specific qeps, hindering her learning process. In this paper, we present a novel, end-to-end, generic sys- tem called lantern that generates a natural language description of a qep to enhance its understanding. It takes as input an sql query and its qep, and generates a natural language description of the execution strategy deployed by the underlying rdbms. Specifi- cally, it deploys a declarative framework called pool that enables subject matter experts to efficiently create and maintain natural language descriptions of physical operators used in qeps. A rule- based framework called rule-lantern is proposed that exploits pool to generate natural language descriptions of qeps. Despite the high accuracy of rule-lantern, our engagement with learners reveal that consistent with existing psychology theories perusing such rule-based descriptions lead to boredom due to repetitive state- ments across different qeps. To address this issue, we present a novel deep learning-based language generation framework called neural-lantern that infuses language variability in the gener- ated description by exploiting a set of paraphrasing tools and word embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of qeps.
Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans
Weiguo Wang, Sourav S, Hui Li, Shafiq Joty, and Siyuan Liu. In Proceedings of 2021 ACM SIGMOD International Conference on Management of Data (SIGMOD'21) , pages x - x, 2021.
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