research-article

SQAK: doing more with keywords

Authors:

Guy M. LohmanAuthors Info & Claims

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

Pages 889 - 902

https://doi.org/10.1145/1376616.1376705

Published: 09 June 2008 Publication History

Abstract

Today's enterprise databases are large and complex, often relating hundreds of entities. Enabling ordinary users to query such databases and derive value from them has been of great interest in database research. Today, keyword search over relational databases allows users to find pieces of information without having to write complicated SQL queries. However, in order to compute even simple aggregates, a user is required to write a SQL statement and can no longer use simple keywords. This not only requires the ordinary user to learn SQL, but also to learn the schema of the complex database in detail in order to correctly construct the required query. This greatly limits the options of the user who wishes to examine a database in more depth.

As a solution to this problem, we propose a framework called SQAK¹ (SQL Aggregates using Keywords) that enables users to pose aggregate queries using simple keywords with little or no knowledge of the schema. SQAK provides a novel and exciting way to trade-off some of the expressive power of SQL in exchange for the ability to express a large class of aggregate queries using simple keywords. SQAK accomplishes this by taking advantage of the data in the database and the schema (tables, attributes, keys, and referential constraints). SQAK does not require any changes to the database engine and can be used with any existing database. We demonstrate using several experiments that SQAK is effective and can be an enormously powerful tool for ordinary users.

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Cited By

Kaur GAgrawal PShelar HAbraha G(2024)Intelligent Search Engine Tool for Querying Database SystemsInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2024.9.4.0489:4(914-930)Online publication date: 1-Aug-2024
https://doi.org/10.33889/IJMEMS.2024.9.4.048
Martins Pda Silva AAfonso ACavalcanti Jde Moura E(2023)Supporting Schema References in Keyword Queries Over Relational DatabasesIEEE Access10.1109/ACCESS.2023.330890811(92365-92390)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3308908
Amsterdamer YCallen Y(2023)Interactive SPARQL query formulation using provenanceKnowledge and Information Systems10.1007/s10115-023-01939-x66:3(2165-2191)Online publication date: 13-Sep-2023
https://doi.org/10.1007/s10115-023-01939-x
Show More Cited By

Index Terms

SQAK: doing more with keywords
1. Information systems

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cover image ACM Conferences

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

June 2008

1396 pages

ISBN:9781605581026

DOI:10.1145/1376616

General Chairs:
Laks V. S. Lakshmanan
University of British Columbia, Canada
,
Raymond T. Ng
University of British Columbia, Canada
,
Dennis Shasha
New York University, USA

Copyright © 2008 ACM.

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Published: 09 June 2008

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SIGMOD/PODS '08

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SIGMOD/PODS '08: SIGMOD/PODS '08 - International Conference on Management of Data

June 9 - 12, 2008

Vancouver, Canada

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Kaur GAgrawal PShelar HAbraha G(2024)Intelligent Search Engine Tool for Querying Database SystemsInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2024.9.4.0489:4(914-930)Online publication date: 1-Aug-2024
https://doi.org/10.33889/IJMEMS.2024.9.4.048
Martins Pda Silva AAfonso ACavalcanti Jde Moura E(2023)Supporting Schema References in Keyword Queries Over Relational DatabasesIEEE Access10.1109/ACCESS.2023.330890811(92365-92390)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3308908
Amsterdamer YCallen Y(2023)Interactive SPARQL query formulation using provenanceKnowledge and Information Systems10.1007/s10115-023-01939-x66:3(2165-2191)Online publication date: 13-Sep-2023
https://doi.org/10.1007/s10115-023-01939-x
Chen YKoudas NYu XYu Z(2022)Spatial and temporal constrained ranked retrieval over videosProceedings of the VLDB Endowment10.14778/3551793.355186515:11(3226-3239)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.14778/3551793.3551865
Saur KMirmira TKaranasos KCamacho-Rodríguez J(2022)Containerized execution of UDFsProceedings of the VLDB Endowment10.14778/3551793.355186015:11(3158-3171)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.14778/3551793.3551860
Gao QLee MLing T(2021)Temporal Keyword Search with Aggregates and Group-ByConceptual Modeling10.1007/978-3-030-89022-3_14(160-175)Online publication date: 16-Oct-2021
https://doi.org/10.1007/978-3-030-89022-3_14
Sen JLei CQuamar AÖzcan FEfthymiou VDalmia AStager GMittal ASaha DSankaranarayanan K(2020)ATHENA++Proceedings of the VLDB Endowment10.14778/3407790.340785813:12(2747-2759)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.14778/3407790.3407858
Gantayat NSaha DSen JMani SDey LChaudhury SKrishnapuram RSingla PRoy R(2019)Goal-based Ontology Creation for Natural Language Querying in SAP-ERP PlatformProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3297001.3297031(231-237)Online publication date: 3-Jan-2019
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Hormozi N(2019)Disambiguation and Result Expansion in Keyword Search Over Relational Databases2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00248(2101-2105)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00248
Baik CJagadish HLi Y(2019)Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00041(374-385)Online publication date: Apr-2019
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