MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

CrowdDB: Query processing with the VLDB crowd

Author(s)
Feng, Amber; Franklin, Michael J.; Kossmann, Donald; Kraska, Tim; Madden, Samuel R.; Ramesh, Sukriti; Wang, Andrew; Xin, Reynold; ... Show more Show less
Thumbnail
DownloadMadden_CrowdDB.pdf (939.7Kb)
OPEN_ACCESS_POLICY

Open Access Policy

Creative Commons Attribution-Noncommercial-Share Alike

Terms of use
Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
Metadata
Show full item record
Abstract
Databases often give incorrect answers when data are missing or semantic understanding of the data is required. Processing such queries requires human input for providing the missing information, for performing computationally difficult functions, and for matching, ranking, or aggregating results based on fuzzy criteria. In this demo we present CrowdDB, a hybrid database system that automatically uses crowdsourcing to integrate human input for processing queries that a normal database system cannot answer. CrowdDB uses SQL both as a language to ask complex queries and as a way to model data stored electronically and provided by human input. Furthermore, queries are automatically compiled and optimized. Special operators provide user interfaces in order to integrate and cleanse human input. Currently CrowdDB supports two crowdsourcing platforms: Amazon Mechanical Turk and our own mobile phone platform. During the demo, the mobile platform will allow the VLDB crowd to participate as workers and help answer otherwise impossible queries.
Date issued
2011-08
URI
http://hdl.handle.net/1721.1/90378
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Feng, Amber, et al. "CrowdDB: Query processing with the VLDB crowd." Proceedings of the VLDB Endowment, Vol. 4, No. 12 (2011).
Version: Author's final manuscript
ISSN
2150-8097

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.