Crowd Database Operators
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.
Crowdsourcing has emerged as a paradigm for...
- 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.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.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.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
- 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
- 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.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.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.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.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.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.Trushkowsky B, Kraska T, Franklin MJ, Sarkar Purnamrita. Crowdsourced enumeration queries. In: Proceedings of the 29th International Conference on Data Engineering; 2013.Google Scholar