Deco: a system for declarative crowdsourcing

Authors:
Hyunjung Park;Hector Garcia-Molina;Richard Pang;Neoklis Polyzotis;Aditya Parameswaran;Jennifer Widom
Affiliations:
Stanford University;Stanford University;Stanford University;UC Santa Cruz;Stanford University;Stanford University
Venue:
Proceedings of the VLDB Endowment
Year:
2012

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Abstract

Deco is a system that enables declarative crowdsourcing: answering SQL queries posed over data gathered from the crowd as well as existing relational data. Deco implements a novel push-pull hybrid execution model in order to support a flexible data model and a precise query semantics, while coping with the combination of latency, monetary cost, and uncertainty of crowdsourcing. We demonstrate Deco using two crowdsourcing platforms: Amazon Mechanical Turk and an in-house platform, to show how Deco provides a convenient means of collecting and querying crowdsourced data.