Challenges in Data Crowdsourcing

被引：93

作者：

Garcia-Molina, Hector ^{[1
]}

Joglekar, Manas ^{[1
]}

Marcus, Adam ^{[2
]}

Parameswaran, Aditya ^{[3
]}

Verroios, Vasilis ^{[1
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[2] Unltd Labs, San Francisco, CA 94103 USA

[3] Univ Illinois UIUC, Dept Comp Sci, Champaign, IL 61820 USA

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2016年 / 28卷 / 04期

关键词：

Data crowdsourcing; data augmenting; data curation; data processing; crowdsourcing space; crowdsourcing design; crowdsourcing workflow; worker management;

D O I：

10.1109/TKDE.2016.2518669

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Crowdsourcing refers to solving large problems by involving human workers that solve component sub-problems or tasks. In data crowdsourcing, the problem involves data acquisition, management, and analysis. In this paper, we provide an overview of data crowdsourcing, giving examples of problems that the authors have tackled, and presenting the key design steps involved in implementing a crowdsourced solution. We also discuss some of the open challenges that remain to be solved.

引用

页码：901 / 911

页数：11

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