Neuron

Query execution plan meets natural language processing for augmenting DB education

Siyuan Liu, Sourav S. Bhowmick, Wanlu Zhang, Shu Wang, Wanyi Huang, Shafiq Rayhan Joty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 a query 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 qeps 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 a natural 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.

Original languageEnglish
Title of host publicationSIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1953-1956
Number of pages4
ISBN (Electronic)9781450356435
DOIs
Publication statusPublished - 25 Jun 2019
Event2019 International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Netherlands
Duration: 30 Jun 20195 Jul 2019

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2019 International Conference on Management of Data, SIGMOD 2019
CountryNetherlands
CityAmsterdam
Period30/6/195/7/19

Fingerprint

Neurons
Education
Students
Processing
Demonstrations
Agglomeration

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Liu, S., Bhowmick, S. S., Zhang, W., Wang, S., Huang, W., & Rayhan Joty, S. (2019). Neuron: Query execution plan meets natural language processing for augmenting DB education. In SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data (pp. 1953-1956). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3299869.3320213

Neuron : Query execution plan meets natural language processing for augmenting DB education. / Liu, Siyuan; Bhowmick, Sourav S.; Zhang, Wanlu; Wang, Shu; Huang, Wanyi; Rayhan Joty, Shafiq.

SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery, 2019. p. 1953-1956 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, S, Bhowmick, SS, Zhang, W, Wang, S, Huang, W & Rayhan Joty, S 2019, Neuron: Query execution plan meets natural language processing for augmenting DB education. in SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data. Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery, pp. 1953-1956, 2019 International Conference on Management of Data, SIGMOD 2019, Amsterdam, Netherlands, 30/6/19. https://doi.org/10.1145/3299869.3320213
Liu S, Bhowmick SS, Zhang W, Wang S, Huang W, Rayhan Joty S. Neuron: Query execution plan meets natural language processing for augmenting DB education. In SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery. 2019. p. 1953-1956. (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/3299869.3320213
Liu, Siyuan ; Bhowmick, Sourav S. ; Zhang, Wanlu ; Wang, Shu ; Huang, Wanyi ; Rayhan Joty, Shafiq. / Neuron : Query execution plan meets natural language processing for augmenting DB education. SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data. Association for Computing Machinery, 2019. pp. 1953-1956 (Proceedings of the ACM SIGMOD International Conference on Management of Data).
@inproceedings{cd442961d5a04a85a751beb5c2613363,
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 a query 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 qeps 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 a natural 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.",
author = "Siyuan Liu and Bhowmick, {Sourav S.} and Wanlu Zhang and Shu Wang and Wanyi Huang and {Rayhan Joty}, Shafiq",
year = "2019",
month = "6",
day = "25",
doi = "10.1145/3299869.3320213",
language = "English",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery",
pages = "1953--1956",
booktitle = "SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data",

}

TY - GEN

T1 - Neuron

T2 - Query execution plan meets natural language processing for augmenting DB education

AU - Liu, Siyuan

AU - Bhowmick, Sourav S.

AU - Zhang, Wanlu

AU - Wang, Shu

AU - Huang, Wanyi

AU - Rayhan Joty, Shafiq

PY - 2019/6/25

Y1 - 2019/6/25

N2 - 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 a query 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 qeps 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 a natural 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.

AB - 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 a query 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 qeps 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 a natural 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.

UR - http://www.scopus.com/inward/record.url?scp=85069541226&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069541226&partnerID=8YFLogxK

U2 - 10.1145/3299869.3320213

DO - 10.1145/3299869.3320213

M3 - Conference contribution

T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data

SP - 1953

EP - 1956

BT - SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data

PB - Association for Computing Machinery

ER -