A core component of a database systems course at the undergraduate level is the design and implementation of the query optimizer in a rdbms. The query optimization process produces a query execution plan (qep) which represents an execution strategy for a sql query. Unfortunately, in practice, it is often difficult for a student to comprehend the query execution strategy by perusing the qep, hindering her learning process. In this demonstration, we present a novel system called neuron that facilitates natural language interaction with qeps to enhance its understanding. neuron accepts a sql query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a simplified natural language-based 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 the qep through a natural language-based question answering framework.We advocate that such tool, world’s first of its kind, can greatly enhance students’ learning of the query optimization topic.

@inproceedings{liu-et-al-sigmod-demo-19, abstract = {A core component of a database systems course at the undergraduate level is the design and implementation of the query optimizer in a rdbms. The query optimization process produces a query execution plan (qep) which represents an execution strategy for a sql query. Unfortunately, in practice, it is often difficult for a student to comprehend the query execution strategy by perusing the qep, hindering her learning process. In this demonstration, we present a novel system called neuron that facilitates natural language interaction with qeps to enhance its understanding. neuron accepts a sql query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a simplified natural language-based 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 the qep through a natural language-based question answering framework.We advocate that such tool, world’s first of its kind, can greatly enhance students’ learning of the query optimization topic.}, address = {Amsterdam, The Netherlands.}, author = {Siyuan Liu and Sourav S Bhowmick and Wanlu Zhang and Shu Wang and Wanyi Huang and Shafiq Joty}, booktitle = {Proceedings of 45th ACM SIGMOD International Conference on Management of Data (Demo)}, month = {July}, pages = {1953–1956}, publisher = {ACM}, series = {SIGMOD'19 (Demo)}, title = {NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education}, url = {papers/liu-et-al-sigmod-demo-19.pdf}, year = {2019} }