%0 Conference Paper
%1 marcus2011crowdsourced
%A Marcus, Adam
%A Wu, Eugene
%A Madden, Samuel
%A Miller, Robert C.
%B Proceedings of the 5th Biennial Conference on Innovative Data Systems Research
%D 2011
%I CIDR
%K cirg collective computing crowdsourcing database intelligence query social
%P 211--214
%T Crowdsourced Databases: Query Processing with People
%U http://dspace.mit.edu/handle/1721.1/62827
%X Amazon's Mechanical Turk (\MTurk") service allows users to post short tasks (\HITs") that other users can receive a small amount of money for completing. Common tasks on the system include labelling a collection of images, com- bining two sets of images to identify people which appear in both, or extracting sentiment from a corpus of text snippets. Designing a work ow of various kinds of HITs for ltering, aggregating, sorting, and joining data sources together is common, and comes with a set of challenges in optimizing the cost per HIT, the overall time to task completion, and the accuracy of MTurk results. We propose Qurk, a novel query system for managing these work ows, allowing crowd- powered processing of relational databases. We describe a number of query execution and optimization challenges, and discuss some potential solutions.