Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Query from examples: an iterative, data-driven approach to query construction

Published: 01 September 2015 Publication History

Abstract

In this paper, we propose a new approach, called Query from Examples (QFE), to help non-expert database users construct SQL queries. Our approach, which is designed for users who might be unfamiliar with SQL, only requires that the user is able to determine whether a given output table is the result of his or her intended query on a given input database. To kick-start the construction of a target query Q, the user first provides a pair of inputs: a sample database D and an output table R which is the result of Q on D. As there will be many candidate queries that transform D to R, QFE winnows this collection by presenting the user with new database-result pairs that distinguish these candidates. Unlike previous approaches that use synthetic data for such pairs, QFE strives to make these distinguishing pairs as close to the original (D,R) pair as possible. By doing so, it seeks to minimize the effort needed by a user to determine if a new database-result pair is consistent with his or her desired query. We demonstrate the effectiveness and efficiency of our approach using real datasets from SQLShare, a cloud-based platform designed to help scientists utilize RDBMS technology for data analysis.

References

[1]
Sloan digital sky survey. http://www.sdss.org/.
[2]
J. Akbarnejad, G. Chatzopoulou, M. Eirinaki, S. Koshy, S. Mittal, D. On, N. Polyzotis, and J. S. V. Varman. SQL QueRIE recommendations. PVLDB, 3(1-2), 2010.
[3]
B. Alexe, L. Chiticariu, R. J. Miller, and W. C. Tan. Muse: Mapping understanding and design by example. In ICDE, 2008.
[4]
B. Alexe, L. Chiticariu, and W.-C. Tan. Spider: A schema mapping debugger. In VLDB, 2006.
[5]
U. Çetintemel, M. Cherniack, J. DeBrabant, Y. Diao, K. Dimitriadou, A. Kalinin, O. Papaemmanouil, and S. B. Zdonik. Query steering for interactive data exploration. In CIDR, 2013.
[6]
G. Chatzopoulou, M. Eirinaki, and N. Polyzotis. Query recommendations for interactive database exploration. In SSDBM, 2009.
[7]
G. Chatzopoulou et al. The QueRIE system for personalized query recommendations. IEEE Data Eng. Bull., 34(2), 2011.
[8]
K. Dimitriadou, O. Papaemmanouil, and Y. Diao. Explore-by-example: An automatic query steering framework for interactive data exploration. In SIGMOD, 2014.
[9]
A. Giacometti, P. Marcel, E. Negre, and A. Soulet. Query recommendations for OLAP discovery driven analysis. In DOLAP, 2009.
[10]
B. Howe, G. Cole, N. Khoussainova, and L. Battle. Automatic starter queries for ad hoc databases. In SIGMOD(demo), 2011.
[11]
B. Howe, G. Cole, E. Souroush, P. Koutris, A. Key, N. Khoussainova, and L. Battle. Database-as-a-service for long-tail science. In SSDBM, 2011.
[12]
N. Khoussainova et al. Snipsuggest: Context-aware autocompletion for SQL. PVLDB, 4(1), 2010.
[13]
N. Khoussainova, Y. Kwon, W.-T. Liao, M. Balazinska, W. Gatterbauer, and D. Suciu. Session-based browsing for more effective query reuse. In SSDBM, 2011.
[14]
H. Li, C.-Y. Chan, and D. Maier. Query from examples: An iterative, data-driven approach to query construction. Technical report, National University of Singapore, August 2015. http://www.comp.nus.edu.sg/~chancy/techreport-august-2015-qfe.pdf.
[15]
H. Mannila and K.-J. Räihä. Automatic generation of test data for relational queries. J. Comput. Syst. Sci., 38(2), 1989.
[16]
F. D. Marchi, S. Lopes, and J.-M. Petit. Efficient algorithms for mining inclusion dependencies. In EDBT, 2002.
[17]
A. Nandi and H. V. Jagadish. Assisted querying using instant-response interfaces. In SIGMOD, 2007.
[18]
C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig Latin: A not-so-foreign language for data processing. In SIGMOD, 2008.
[19]
L. Qian, M. J. Cafarella, and H. V. Jagadish. Sample-driven schema mapping. In SIGMOD, 2012.
[20]
S. Shah et al. Generating test data for killing SQL mutants: A constraint-based approach. In ICDE, 2011.
[21]
Q. T. Tran, C.-Y. Chan, and S. Parthasarathy. Query reverse engineering. The VLDB Journal, 23(5), 2014.
[22]
K. Yessenov, S. Tulsiani, A. Menon, R. C. Miller, S. Gulwani, B. Lampson, and A. Kalai. A colorful approach to text processing by example. In UIST, 2013.
[23]
M. Zhang, H. Elmeleegy, C. M. Procopiuc, and D. Srivastava. Reverse engineering complex join queries. In SIGMOD, 2013.

Cited By

View all

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment  Volume 8, Issue 13
Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii
September 2015
144 pages
ISSN:2150-8097
Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 September 2015
Published in PVLDB Volume 8, Issue 13

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)42
  • Downloads (Last 6 weeks)6
Reflects downloads up to 24 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Data-Driven Insight Synthesis for Multi-Dimensional DataProceedings of the VLDB Endowment10.14778/3641204.364121117:5(1007-1019)Online publication date: 1-Jan-2024
  • (2024)Fitting Algorithms for Conjunctive QueriesACM SIGMOD Record10.1145/3641832.364183452:4(6-18)Online publication date: 19-Jan-2024
  • (2024)Wred: Workload Reduction for Scalable Index TuningProceedings of the ACM on Management of Data10.1145/36393052:1(1-26)Online publication date: 26-Mar-2024
  • (2024)Querying knowledge graphs through positive and negative examples and feedbackJournal of Intelligent Information Systems10.1007/s10844-024-00846-z62:5(1165-1186)Online publication date: 1-Oct-2024
  • (2024)Unifying Faceted Search and Analytics over RDF Knowledge GraphsKnowledge and Information Systems10.1007/s10115-024-02076-966:7(3921-3958)Online publication date: 1-Jul-2024
  • (2024)Efficient and robust active learning methods for interactive database explorationThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00816-x33:4(931-956)Online publication date: 1-Jul-2024
  • (2024)Towards Reliable SQL Synthesis: Fuzzing-Based Evaluation and DisambiguationFundamental Approaches to Software Engineering10.1007/978-3-031-57259-3_11(232-254)Online publication date: 6-Apr-2024
  • (2023)FormaT5: Abstention and Examples for Conditional Table Formatting with Natural LanguageProceedings of the VLDB Endowment10.14778/3632093.363211117:3(497-510)Online publication date: 1-Nov-2023
  • (2023)EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User InteractionsProceedings of the VLDB Endowment10.14778/3611479.361148216:11(2714-2727)Online publication date: 1-Jul-2023
  • (2023)Cornet: Learning Table Formatting Rules By ExampleProceedings of the VLDB Endowment10.14778/3603581.360360016:10(2632-2644)Online publication date: 1-Jun-2023
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media