Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing
Contribuinte(s) |
University of Aberdeen, Natural & Computing Sciences, Computing Science University of Aberdeen, dot.rural Digital Economy Hub |
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Data(s) |
27/09/2016
27/09/2016
01/10/2016
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Resumo |
ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Peer reviewed Postprint |
Formato |
14 |
Identificador |
Ouyang , R W , Kaplan , L , Toniolo , A , Srivastava , M & Norman , T J F 2016 , ' Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing ' IEEE Transactions on Parallel and Distributed Systems , vol 27 , no. 10 , pp. 2984-2997 . , 10.1109/TPDS.2016.2515092 1045-9219 PURE: 60661399 PURE UUID: 80c4f978-2a67-4d92-81a6-308917ec9c12 |
Idioma(s) |
eng |
Relação |
IEEE Transactions on Parallel and Distributed Systems |
Direitos |
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating newcollective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Palavras-Chave | #Crowdsourcing #truth discovery #quantitative task #big data #parallel algorithm #streaming algorithm #QA75 Electronic computers. Computer science #Computer Science(all) #QA75 |
Tipo |
Journal article |