Paper Info
Title
NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education
Abstract
A core component of a database systems course at the undergraduate level is the design and implementation of the query optimizer in an rdbms. The query optimization process produces aquery execution plan (qep ), which represents an execution strategy for an sql query. Unfortunately, in practice, it is often difficult for a student to comprehend a query execution strategy by perusing its qep, hindering her learning process. In this demonstration, we present a novel system called neuron that facilitates natural language interaction with qep s to enhance its understanding. neuron accepts an sql query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a simplified natural language description (both in text and voice form) of the execution strategy deployed by the underlying rdbms. Furthermore, it facilitates understanding of various features related to a qep through anatural language question answering (nlqa ) framework. We advocate that such tool, world's first of its kind, can greatly enhance students' learning of the query optimization topic.
Year
DOI
Venue
2019
10.1145/3299869.3320213
Proceedings of the 2019 International Conference on Management of Data
Keywords
Field
DocType
database education, database usability, natural language text generation, query execution plan, question answering framework, relational database
SQL,Query optimization,Joins,Programming language,Question answering,Natural language interaction,Relational database,Computer science,Natural language,Relational database management system,Database
Conference
ISSN
ISBN
Citations 
0730-8078
978-1-4503-5643-5
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Siyuan Liu102.03
Sourav S. Bhowmick21519272.35
Wanlu Zhang301.35
Shu Wang422828.72
Wanyi Huang500.68
Shafiq R. Joty656056.72