skip to main content
10.1145/3077257.3077270acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

A Game-theoretic Approach to Data Interaction: A Progress Report

Published:14 May 2017Publication History

ABSTRACT

As most database users cannot precisely express their information needs in the form of database queries, it is challenging for database query interfaces to understand and satisfy their intents. Database systems usually improve their understanding of users' intents by collecting their feedback on the answers to the users' imprecise and ill-specified queries. Users may also learn to express their queries precisely during their interactions with the database system. In this paper, we report our progress on developing a formal framework for representing and understanding information needs in database querying and exploration. Our framework considers querying as a collaboration between the user and the database system to establish a mutual language for representing information needs. We formalize this collaboration as a signaling game between two potentially rational agents: the user and the database system. We believe that this framework naturally models the long-term interaction of users and database systems.

References

  1. Azza Abouzied, Dana Angluin, Christos H. Papadimitriou, Joseph M. Hellerstein, and Avi Silberschatz. 2013. Learning and verifying quantified boolean queries by example. In PODS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Paolo Avesani and Marco Cova. 2005. Shared lexicon for distributed annotations on the Web. In WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. A. Barrett and K. Zollman. 2008. The Role of Forgetting in the Evolution and Learning of Language. Journal of Experimental and Theoretical Artificial Intelligence 21, 4 (2008), 293--309.Google ScholarGoogle ScholarCross RefCross Ref
  4. Thomas Beckers and others. 2010. Report on INEX 2009. SIGIR Forum 44, 1 (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Angela Bonifati, Radu Ciucanu, and Slawomir Staworko. 2015. Learning Join Queries from User Examples. TODS 40, 4 (2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, and Gerhard Weikum. 2006. Probabilistic Information Retrieval Approach for Ranking of Database Query Results. TODS 31, 3 (2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Yi Chen, Wei Wang, Ziyang Liu, and Xuemin Lin. 2009. Keyword Search on Structured and Semi-structured Data. In SIGMOD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. I. Cho and D. Kreps. 1987. Signaling games and stable equilibria. Quarterly Journal of Economics 102 (1987).Google ScholarGoogle Scholar
  9. Norbert Fuhr and Thomas Rolleke. 1997. A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems. TOIS 15 (1997). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Vagelis Hristidis, Luis Gravano, and Yannis Papakonstantinou. Efficient IR-Style Keyword Search over Relational Databases. In VLDB 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Stratos Idreos, Olga Papaemmanouil, and Surajit Chaudhuri. 2015. Overview of Data Exploration Techniques. In SIGMOD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. V. Jagadish, Adriane Chapman, Aaron Elkiss, Magesh Jayapandian, Yunyao Li, Arnab Nandi, and Cong Yu. 2007. Making Database Systems Usable. In SIGMOD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Nodira Khoussainova, YongChul Kwon, Magdalena Balazinska, and Dan Suciu. 2010. SnipSuggest: Context-aware Autocompletion for SQL. PVLDB 4, 1 (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. David Lewis. 1969. Convention. Cambridge: Harvard University Press.Google ScholarGoogle Scholar
  15. Hao Li, Chee-Yong Chan, and David Maier. 2015. Query From Examples: An Iterative, Data-Driven Approach to Query Construction. PVLDB 8, 13 (2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jiyun Luo, Sicong Zhang, and Hui Yang. 2014. Win-Win Search: Dual-Agent Stochastic Game in Session Search. In SIGIR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Christopher Manning, Prabhakar Raghavan, and Hinrich Schutze. 2008. An Introduction to Information Retrieval. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ben McCamish, Arash Termehchy, Behrouz Touri, and Eduardo Cotilla Sanchez. 2016. A Signaling Game Approach to Databases Querying. In AMW.Google ScholarGoogle Scholar
  19. Aleksandr Vorobev, Damien Lefortier, Gleb Gusev, and Pavel Serdyukov. 2015. Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking. In WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Yahoo! 2011. Yahoo! webscope dataset anonymized Yahoo! search logs with relevance judgments version 1.0. http://labs.yahoo.com/Academic_Relations. (2011). {Online; accessed 5-January-2017}.Google ScholarGoogle Scholar
  21. Yinan Zhang and Chengxiang Zhai. 2015. Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface. In SIGIR. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

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

Sign in
  • Published in

    cover image ACM Conferences
    HILDA '17: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics
    May 2017
    89 pages
    ISBN:9781450350297
    DOI:10.1145/3077257

    Copyright © 2017 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 14 May 2017

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate28of56submissions,50%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader