research-article

CrowdDB: query processing with the VLDB crowd

Authors:

Reynold XinAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 4, Issue 12

Pages 1387 - 1390

https://doi.org/10.14778/3402755.3402777

Published: 01 August 2011 Publication History

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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.

References

[1]

Amazon Mechanical Turk. http://www.mturk.com, 2010.

Google Scholar

[2]

CrowdDB Mobile Service. http://www.crowddb.org/mobile.

Google Scholar

[3]

M. Franklin, D. Kossmann, T. Kraska, S. Ramesh, and R. Xin. CrowdDB: Answering Queries with Crowdsourcing. In SIGMOD, pages 61--72, 2011.

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Google Scholar

[4]

H2 Database Engine. http://www.h2database.com/.

Google Scholar

[5]

A. Marcus, E. Wu, D. Karger, S. Madden, and R. C. Miller. Demonstration of Qurk: A Query Processor for Human Operators. In SIGMOD, pages 1315--1318, 2011.

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Cited By

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Madden SCafarella MFranklin MKraska T(2024)Databases Unbound: Querying All of the World's Bytes with AIProceedings of the VLDB Endowment10.14778/3685800.368591617:12(4546-4554)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685916
Zhang CLiu YZeng PWu TChen LHui PHao F(2024)Similarity-driven and task-driven models for diversity of opinion in crowdsourcing marketsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00853-033:5(1377-1398)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00778-024-00853-0
Yang KGao YLiang LBian SChen LZheng B(2021)CrowdTC: Crowd-powered Learning for Text ClassificationACM Transactions on Knowledge Discovery from Data10.1145/345721616:1(1-23)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3457216
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CrowdDB: query processing with the VLDB crowd
1. Information systems
  1. Data management systems
    1. Database management system engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 4, Issue 12

August 2011

303 pages

ISSN:2150-8097

Editor:
H. V. Jagadish

Issue’s Table of Contents

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VLDB Endowment

Publication History

Published: 01 August 2011

Published in PVLDB Volume 4, Issue 12

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Madden SCafarella MFranklin MKraska T(2024)Databases Unbound: Querying All of the World's Bytes with AIProceedings of the VLDB Endowment10.14778/3685800.368591617:12(4546-4554)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685916
Zhang CLiu YZeng PWu TChen LHui PHao F(2024)Similarity-driven and task-driven models for diversity of opinion in crowdsourcing marketsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-024-00853-033:5(1377-1398)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00778-024-00853-0
Yang KGao YLiang LBian SChen LZheng B(2021)CrowdTC: Crowd-powered Learning for Text ClassificationACM Transactions on Knowledge Discovery from Data10.1145/345721616:1(1-23)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3457216
Jiang NZhuang YChiu D(2020)Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systemsMultimedia Tools and Applications10.1007/s11042-020-08755-379:27-28(19893-19923)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s11042-020-08755-3
Salam MKoone MThirumuruganathan SDas GBasu Roy S(2019)A Human-in-the-loop Attribute Design Framework for ClassificationThe World Wide Web Conference10.1145/3308558.3313547(1612-1622)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313547
Li YKou NWang HU LGong Z(2017)A confidence-aware top-k query processing toolkit on crowdsourcingProceedings of the VLDB Endowment10.14778/3137765.313780610:12(1909-1912)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.14778/3137765.3137806
Ikeda KMorishima ARahman HRoy SThirumuruganathan SAmer-Yahia SDas G(2016)Collaborative crowdsourcing with crowd4UProceedings of the VLDB Endowment10.14778/3007263.30072939:13(1497-1500)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.14778/3007263.3007293
Khan AGarcia-Molina HMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Attribute-based Crowd Entity ResolutionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983831(549-558)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983831
Wu TChen LHui PZhang CLi W(2015)Hear the whole storyProceedings of the VLDB Endowment10.14778/2735479.27354828:5(485-496)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.14778/2735479.2735482
Zhang CMeng RChen LZhu F(2015)CrowdLinkProceedings of the Second International Workshop on Exploratory Search in Databases and the Web10.1145/2795218.2795222(15-20)Online publication date: 31-May-2015
https://dl.acm.org/doi/10.1145/2795218.2795222
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