CrowdDB: Query processing with the VLDB crowd
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 ba...
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| Format: | Article |
| Language: | en_US |
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VLDB Endowment
2014
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| Online Access: | http://hdl.handle.net/1721.1/90378 https://orcid.org/0000-0002-7470-3265 |
| _version_ | 1826191732538081280 |
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| author | Feng, Amber Franklin, Michael J. Kossmann, Donald Kraska, Tim Madden, Samuel R. Ramesh, Sukriti Wang, Andrew Xin, Reynold |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Feng, Amber Franklin, Michael J. Kossmann, Donald Kraska, Tim Madden, Samuel R. Ramesh, Sukriti Wang, Andrew Xin, Reynold |
| author_sort | Feng, Amber |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:00:24Z |
| format | Article |
| id | mit-1721.1/90378 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:00:24Z |
| publishDate | 2014 |
| publisher | VLDB Endowment |
| record_format | dspace |
| spelling | mit-1721.1/903782022-09-26T09:46:31Z CrowdDB: Query processing with the VLDB crowd Feng, Amber Franklin, Michael J. Kossmann, Donald Kraska, Tim Madden, Samuel R. Ramesh, Sukriti Wang, Andrew Xin, Reynold Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Madden, Samuel R. 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. 2014-09-26T12:16:19Z 2014-09-26T12:16:19Z 2011-08 Article http://purl.org/eprint/type/JournalArticle 2150-8097 http://hdl.handle.net/1721.1/90378 Feng, Amber, et al. "CrowdDB: Query processing with the VLDB crowd." Proceedings of the VLDB Endowment, Vol. 4, No. 12 (2011). https://orcid.org/0000-0002-7470-3265 en_US http://www.vldb.org/pvldb/vol4.html Proceedings of the VLDB Endowment Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf VLDB Endowment Other univ. web domain |
| spellingShingle | Feng, Amber Franklin, Michael J. Kossmann, Donald Kraska, Tim Madden, Samuel R. Ramesh, Sukriti Wang, Andrew Xin, Reynold CrowdDB: Query processing with the VLDB crowd |
| title | CrowdDB: Query processing with the VLDB crowd |
| title_full | CrowdDB: Query processing with the VLDB crowd |
| title_fullStr | CrowdDB: Query processing with the VLDB crowd |
| title_full_unstemmed | CrowdDB: Query processing with the VLDB crowd |
| title_short | CrowdDB: Query processing with the VLDB crowd |
| title_sort | crowddb query processing with the vldb crowd |
| url | http://hdl.handle.net/1721.1/90378 https://orcid.org/0000-0002-7470-3265 |
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