CrowdDB: Query processing with the VLDB crowd
Name
Madden_CrowdDB.pdf
Size
939.74 KB
Format
Adobe PDF
Checksum (MD5)
bcff5212dc2a5fcabd104005c367b9c4
Author(s)
Feng, Amber
Franklin, Michael J.
Kossmann, Donald
Kraska, Tim
Madden, Samuel R.
Ramesh, Sukriti
Wang, Andrew
Xin, Reynold
Date Issued
August 2011
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://www.vldb.org/pvldb/vol4.html
