research-article

SQL QueRIE recommendations

Editors: Elisa Bertino, Paolo Atzeni, Kian Lee Tan, Yi Chen, Y. C. Tay Authors:

Duc On,

Jothi S. Vindhiya VarmanAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 3, Issue 1-2

Pages 1597 - 1600

https://doi.org/10.14778/1920841.1921048

Published: 01 September 2010 Publication History

Get Access

Abstract

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation algorithms. The first identifies potentially "interesting" parts of the database related to the corresponding data analysis task by locating those database parts that were accessed by similar users in the past. The second identifies structurally similar queries to the ones posted by the current user. Both approaches result in a recommendation set of SQL queries that is provided to the user to modify, or directly post to the database. The demonstrated system will enable users to query and get real-time recommendations from the SkyServer database, using user traces collected from the SkyServer query log.

References

[1]

G. Chatzopoulou, M. Eirinaki, and N. Polyzotis. Collaborative filtering for interactive database exploration. In SSDBM '09.

Digital Library

Google Scholar

[2]

E. Cohen. Size-estimation framework with applications to transitive closure and reachability. Journal of Computer and System Sciences, 55:441--453, 1997.

Digital Library

Google Scholar

[3]

A. Giacometti, P. Marcel, and E. Negre. Recommending Multidimensional Queries. In DaWaK'09.

Digital Library

Google Scholar

[4]

A. Giacometti, P. Marcel, E. Negre, and A. Soulet. Query recommendations for olap discovery driven analysis. In DOLAP '09.

Digital Library

Google Scholar

[5]

N. Khoussainova, M. Balazinska, W. Gatterbauer, Y. Kwon, and D. Suciu. A case for a collaborative query management system. In CIDR '09.

Google Scholar

[6]

G. Koutrika and Y. Ioannidis. Personalized queries under a generalized preference model. In ICDE '05.

Digital Library

Google Scholar

[7]

S. Mittal, J. S. V. Varman, G. Chatzopoulou, M. Eirinaki, and N. Polyzotis. QueRIE: A Recommender System supporting Interactive Database Exploration. In ICDM '09 (to appear in ICDM '10 proceedings because of editor's error).

Digital Library

Google Scholar

Cited By

View all

Chen SHe YCui WFan JGe SZhang HZhang DChaudhuri S(2024)Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table RepresentationsProceedings of the ACM on Management of Data10.1145/36549252:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654925
Gatterbauer W(2020)Databases will visualize queries tooProceedings of the VLDB Endowment10.14778/3402755.34028054:12(1498-1501)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402755.3402805
Leventidis AZhang JDunne CGatterbauer WJagadish HRiedewald MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)QueryVis: Logic-based Diagrams help Users Understand Complicated SQL Queries FasterProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389767(2303-2318)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389767
Show More Cited By

SQL QueRIE recommendations
1. Information systems
  1. Data management systems

Recommendations

QueRIE: A Query Recommender System Supporting Interactive Database Exploration
ICDMW '10: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by generating personalized query recommendations. Drawing inspiration from ...
Naïve filterbots for robust cold-start recommendations
KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any ...
Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...

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 3, Issue 1-2

September 2010

1658 pages

ISSN:2150-8097

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 September 2010

Published in PVLDB Volume 3, Issue 1-2

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

22
Total Citations
View Citations
280
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chen SHe YCui WFan JGe SZhang HZhang DChaudhuri S(2024)Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table RepresentationsProceedings of the ACM on Management of Data10.1145/36549252:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654925
Gatterbauer W(2020)Databases will visualize queries tooProceedings of the VLDB Endowment10.14778/3402755.34028054:12(1498-1501)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402755.3402805
Leventidis AZhang JDunne CGatterbauer WJagadish HRiedewald MMaier DPottinger RDoan ATan WAlawini ANgo H(2020)QueryVis: Logic-based Diagrams help Users Understand Complicated SQL Queries FasterProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389767(2303-2318)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389767
Liu JZolaktaf ZPottinger RMilani MMaltzahn CMalik T(2019)Improvement of SQL Recommendation on Scientific DatabaseProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335800(206-209)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3335783.3335800
Zhang QZhang HSellam TWu EBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Mining Precision Interfaces From Query LogsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319872(988-1005)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3319872
Naseriparsa MLiu CIslam MZhou R(2019)XPloreRankWorld Wide Web10.1007/s11280-018-0630-x22:4(1727-1750)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11280-018-0630-x
Tex CSchäler MBöhm KSacharidis DGamper JBöhlen M(2018)Towards meaningful distance-preserving encryptionProceedings of the 30th International Conference on Scientific and Statistical Database Management10.1145/3221269.3223029(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3221269.3223029
Seabolt EKandogan ERoth M(2018)Contextual Intelligence for Unified Data GovernanceProceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management10.1145/3211954.3211955(1-9)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3211954.3211955
Naseriparsa MIslam MLiu CMoser I(2018)No-but-semantic-matchWorld Wide Web10.1007/s11280-017-0503-821:5(1223-1257)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11280-017-0503-8
Christodoulakis CKandogan ETerrizzano IMiller RGlowacka DMilios ESoto APaulovich F(2017)VIQSProceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics10.1145/3038462.3038470(25-32)Online publication date: 13-Mar-2017
https://dl.acm.org/doi/10.1145/3038462.3038470
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

QueRIE: A Query Recommender System Supporting Interactive Database Exploration

Naïve filterbots for robust cold-start recommendations

Integrating collaborative filtering and matching-based search for product recommendations