CrowdDB

Author:

Feng Amber1,Franklin Michael1,Kossmann Donald2,Kraska Tim1,Madden Samuel3,Ramesh Sukriti2,Wang Andrew1,Xin Reynold1

Affiliation:

1. UC Berkeley

2. ETH Zurich

3. CSAIL, MIT

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.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Crowdsourcing as a Future Collaborative Computing Paradigm;Wireless Networks;2023

2. CrowdTC: Crowd-powered Learning for Text Classification;ACM Transactions on Knowledge Discovery from Data;2021-07-03

3. Fluid;Proceedings of the 2019 International Conference on Management of Data;2019-06-25

4. Crowd Database Systems;Encyclopedia of Database Systems;2018

5. Integration of graphs from different data sources using crowdsourcing;Information Sciences;2017-04

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