Crowdsourced Databases: Query Processing with People
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Madden_Crowdsourced databases.pdf
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Author(s)
Marcus, Adam
Wu, Eugene
Karger, David R.
Madden, Samuel R.
Miller, Robert C.
Date Issued
January 2011
Journal
Proceedings of the 5th Biennial Conference on Innovative Data Systems Research (CIDR 2011)
Publisher
CIDR
Citation
Marcus, Adam, et al."Crowdsourced Databases: Query Processing with People." 5th Biennial Conference on Innovative Data Systems Research (CIDR '11) January 9-12, 2011, Asilomar, California, USA
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
http://www.cidrdb.org/cidr2011/program.html
