Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Crowd Database Operators

  • Beth TrushkowskyEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80660


Crowd-based operators; Crowd-powered operators


Crowd database operators are query plan operators in which all or part of the computation is done by humans, via crowdsourcing. They are alternate implementations of traditional relational operators, like sort or select, for use in hybrid human/machine query processing systems like crowd database systems. The use of crowdsourcing enables these systems to perform query operations that are well suited for people to compute, such as subjective comparisons, fuzzy matching for predicates and joins, entity resolution, etc., that leverage human perception, knowledge, and experience. The implementation of these operators typically includes user interfaces for collecting input from crowd workers, strategies to combine data received from multiple workers, as well as techniques to balance the cost of paying workers and the quality of the operator’s output.

Historical Background

Crowdsourcing has emerged as a paradigm for...

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Recommended Reading

  1. 1.
    Franklin MJ, Kossmann D, Kraska T, Ramesh S, Xin R. CrowdDB: answering queries with crowdsourcing. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2011.Google Scholar
  2. 2.
    Marcus A, Wu E, Madden S, Miller R. Crowdsourced databases: query processing with people. In: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research; 2011.Google Scholar
  3. 3.
    Parameswaran AG, Park H, Garcia-Molina H, Polyzotis N, Widom J. Deco: declarative crowdsourcing. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management; 2012.Google Scholar
  4. 4.
    Parameswaran AG, Garcia-Molina H, Park H, Polyzotis N, Ramesh A, Widom J. Crowdscreen: algorithms for filtering data with humans. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2012.Google Scholar
  5. 5.
    Parameswaran A, Boyd S, Garcia-Molina H, Gupta A, Polyzotis N, Widom J. Optimal crowd-powered rating and filtering algorithms. Proc VLDB Endowment. 2014;7(9):685–696.CrossRefGoogle Scholar
  6. 6.
    Das Sarma A, Parameswaran A, Garcia-Molina H, Halevy A. Crowd-powered find algorithms. In: Proceeings of the IEEE International Conference on Data Engineering.Google Scholar
  7. 7.
    Marcus A, Wu E, Karger D, Madden S, Miller R. Human-powered sorts and joins. Proc VLDB Endowment 2011;5(1):13–24.CrossRefGoogle Scholar
  8. 8.
    Polychronopoulos V, de Alfaro L, Davis J, Garcia-Molina H, Polyzotis N. Human – powered top-k lists. In: Proceedings of the 11th International Workshop on the World Wide Web and Databases; 2013.Google Scholar
  9. 9.
    Davidson SB, Khanna S, Milo T, Roy S. Using the crowd for top-k and group-by queries. In: Proceedings of the 15th International Conference on Database Theory; 2013.Google Scholar
  10. 10.
    Guo S, Parameswaran A, Garcia-Molina H. So who won? Dynamic max discovery with the crowd. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2012.Google Scholar
  11. 11.
    Venetis P, Garcia-Molina H, Huang K, Polyzotis N. Max algorithms in crowdsourcing environments. In: Proceedings of the 21st international conference on World Wide Web; 2012.Google Scholar
  12. 12.
    Gomes R, Welinder P, Krause A, Perona P. Crowdclustering. In: Advances in Neural Information Proceedings of the Systems 24, Proceedings of the 25th Annual Conference on Neural Information Proceedings of the Systems; 2011.Google Scholar
  13. 13.
    Ipeirotis PG, Provost F, Wang J. Quality management on Amazon mechanical turk. In: Proceedings of the ACM SIGKDD Workshop on Human Computation, 2010.Google Scholar
  14. 14.
    Trushkowsky B, Kraska T, Franklin MJ, Sarkar Purnamrita. Crowdsourced enumeration queries. In: Proceedings of the 29th International Conference on Data Engineering; 2013.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceHarvey Mudd CollegeClaremontUSA

Section editors and affiliations

  • Reynold Cheng
    • 1
  1. 1.Computer ScienceThe University of Hong KongHong KongChina