Crowdsourced Databases: Query Processing with People
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 ap...
| Principais autores: | , , , , |
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| Outros Autores: | |
| Formato: | Artigo |
| Idioma: | en_US |
| Publicado em: |
CIDR
2011
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| Acesso em linha: | http://hdl.handle.net/1721.1/62827 https://orcid.org/0000-0002-7470-3265 https://orcid.org/0000-0002-0024-5847 https://orcid.org/0000-0002-0442-691X |
| Resumo: | 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. |
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