Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

doi:10.1109/TPDS.2016.2515092

	Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing
	Ouyang, Robin Wentao 1; Kaplan, Lance M.2; Toniolo, Alice 3; Srivastava, Mani 4,5; Norman, Timothy J.3
	2016-10-01
发表期刊	IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
ISSN	1045-9219
卷号	27 期号:10 页码:2984-2997
摘要	To enable reliable crowdsourcing applications, it is of great importance to develop algorithms that can automatically discover the truths from possibly noisy and conflicting claims provided by various information sources. In order to handle crowdsourcing applications involving big or streaming data, a desirable truth discovery algorithm should not only be effective, but also be scalable. However, with respect to quantitative crowdsourcing applications such as object counting and percentage annotation, existing truth discovery algorithms are not simultaneously effective and scalable. They either address truth discovery in categorical crowdsourcing or perform batch processing that does not scale. In this paper, we propose new parallel and streaming truth discovery algorithms for quantitative crowdsourcing applications. Through extensive experiments on real-world and synthetic datasets, we demonstrate that 1) both of them are quite effective, 2) the parallel algorithm can efficiently perform truth discovery on large datasets, and 3) the streaming algorithm processes data incrementally, and it can efficiently perform truth discovery both on large datasets and in data streams.
关键词	Crowdsourcing truth discovery quantitative task big data parallel algorithm streaming algorithm
DOI	10.1109/TPDS.2016.2515092
收录类别	SCI
语种	英语
资助项目	U.S. ARL ; U.K. Ministry of Defense[W911NF-06-3-0001] ; NSF[CNS-1213140]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000384239300015
出版者	IEEE COMPUTER SOC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8128
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ouyang, Robin Wentao
作者单位	1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China 2.US Army Res Lab, Networked Sensing Fus Branch, Adelphi, MD 20783 USA 3.Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland 4.Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90024 USA 5.Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA
推荐引用方式 GB/T 7714	Ouyang, Robin Wentao,Kaplan, Lance M.,Toniolo, Alice,et al. Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2016,27(10):2984-2997.
APA	Ouyang, Robin Wentao,Kaplan, Lance M.,Toniolo, Alice,Srivastava, Mani,&Norman, Timothy J..(2016).Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,27(10),2984-2997.
MLA	Ouyang, Robin Wentao,et al."Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 27.10(2016):2984-2997.