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Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

文献类型：期刊论文

作者Ouyang, Robin Wentao1; Kaplan, Lance M.2; Toniolo, Alice5; Srivastava, Mani3,4; Norman, Timothy J.5
刊名IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
出版日期2016-10-01
卷号27期号:10页码:2984-2997
关键词Crowdsourcing truth discovery quantitative task big data parallel algorithm streaming algorithm
ISSN号1045-9219
DOI10.1109/TPDS.2016.2515092
英文摘要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.
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
语种英语
WOS记录号WOS:000384239300015
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/8127]  
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 Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90024 USA
4.Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA
5.Univ Aberdeen, Dept Comp Sci, Aberdeen, Scotland
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.

入库方式： OAI收割

来源：计算技术研究所

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