Encyclopedia of Database Systems

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

Crowd Database Systems

  • Ju Fan
  • Meihui Zhang
  • Beng Chin Ooi
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80738

Synonyms

Crowd-powered database systems; Crowdsourcing data analytics systems; Declarative crowdsourcing systems; Human-powered database systems

Definition

Crowdsourcing database systems are designed to add crowd functionality into traditional database management systems (DBMSs) for processing queries that cannot be answered by machines only. The systems typically take declarative queries written in SQL-like query language as input and process over stored relational data together with the collective knowledge gathered on-demand from the crowd. A typical crowdsourcing database system includes a query parser, which compiles the input query; a query optimizer, which generates the optimized query plan; an executor, which manages the query execution; and an HIT manager, which interacts with the public crowd.

Historical Background

While relational database system offers a powerful tool for data management, it imposes limitations in some situations. One situation is when there is missing...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Feng A, Franklin MJ, Kossmann D, Kraska T, Madden S, Ramesh S, Wang A, Xin R. CrowdDB: query processing with the VLDB crowd. Proc VLDB Endowment. 2011;4(12):1387–90.Google Scholar
  2. 2.
    Franklin MJ, Kossmann D, Kraska T, Ramesh S, Xin R. CrowdDB: answering queries with crowdsourcing. In: Proceedings of the SIGMOD Conference; 2011. p. 61–72.Google Scholar
  3. 3.
    Marcus A, Wu E, Karger DR, Madden S, Miller RC. Demonstration of Qurk: a query processor for humanoperators. In: Proceedings of the SIGMOD Conference; 2011. p. 1315–8.Google Scholar
  4. 4.
    Marcus A, Wu E, Madden S, Miller RC. Crowdsourced databases: query processing with people. In: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research; 2011. p. 211–4.Google Scholar
  5. 5.
    Marcus A, Wu E, Karger DR, Madden S, Miller RC. Human-powered sorts and joins. Proc VLDB Endowment. 2011;5(1):13–24.CrossRefGoogle Scholar
  6. 6.
    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. p. 1203–12.Google Scholar
  7. 7.
    Park H, Widom J. Query optimization over crowdsourced data. Proc VLDB Endowment. 2013;6(10):781–92.CrossRefGoogle Scholar
  8. 8.
    Fan J, Lu M, Ooi BC, Tan W-C, Zhang M. A hybrid machine-crowdsourcing system for matching web tables. In: Proceedings of the 30th International Conference on Data Engineering; 2014. p. 976–87.Google Scholar
  9. 9.
    Gao J, Liu X, Ooi BC, Wang H, Chen G. An online cost sensitive decision-making method in crowdsourcing systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2013. p. 217–28.Google Scholar
  10. 10.
    Liu X, Lu M, Ooi BC, Shen Y, Wu S, Zhang M. CDAS: a crowdsourcing data analytics system. Proc VLDB Endowment. 2012;5(10):1040–51.CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Kumar KS, Triantafillou P, Weikum G. Combining information extraction and human computing for crowdsourced knowledge acquisition. In: Proceedings of the 30th International Conference on Data Engineering; 2014. p. 988–99.Google Scholar
  13. 13.
    Wang J, Kraska T, Franklin MJ, Feng J. CrowdER: crowdsourcing entity resolution. Proc VLDB Endowment. 2012;5(11):1483–94.CrossRefGoogle Scholar
  14. 14.
    Doan AH, Ramakrishnan R, Halevy AY. Crowdsourcing systems on the World-Wide Web. Commun ACM. 2011;54(4):86–96.CrossRefGoogle Scholar
  15. 15.
    Fan J, Li G, Ooi BC, Tan KL, Feng J. iCrowd: an adaptive crowdsourcing framework. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2015. p. 1015–30.Google Scholar
  16. 16.
    Fan J, Zhang M, Kok S, Lu M, Ooi BC. CrowdOp: query optimization for declarative crowdsourcing systems. IEEE Trans Knowl Data Eng. 2015;27(8):2078–92.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.DEKE Lab and School of InformationRenmin University of ChinaBeijingChina
  2. 2.Information Systems Technology and DesignSingapore University of Technology and DesignSingaporeSingapore
  3. 3.School of ComputingNational University of SingaporeSingaporeSingapore

Section editors and affiliations

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