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SUMMARY:Managing Quality of Crowdsourced Data
DTSTART:20160824T100000
DTEND:20160824T120000
DTSTAMP:20260414T234446Z
UID:b69a4efe72c37f60ad956fe65c22c12f7d5036328d9867a933e92ed9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Naman Goel\nEDIC Candidacy Exam\nExam President: Prof. Marcel 
 Salathé\nThesis Director: Prof. Boi Faltings\nCo-examiner: Prof. Daniel G
 atica-Perez\nBackground papers:Eliciting Informative Feedback: The Peer-Pr
 ediction Method\, by N. Miller\, P. Resnick\, R. ZeckhauserTruth Discovery
  with Multiple Conflicting Information Providers on the Web\, by X. Yin\, 
 J. Han\, P. YuIncentive Schemes for Participatory Sensing\, by G. Radanovi
 c\, B. Faltings.Abstract\nCollecting data from people has become easier wi
 th web based technologies. However\, ensuring the quality of this data is 
 difficult. The problem isn’t limited to data elicited on crowdsourcing p
 latforms. Web is dominated by user-generated content. This content is copi
 ed/shared/endorsed by various sources. Presence of untrustworthy sources r
 esults in low quality data. We consider one dimension of the solution spac
 e which deals with incentivizing people to report correct data. This inclu
 des designing payment mechanisms with no access to truth. A specific appli
 cation scenario is participatory sensing\, where a spatially distributed p
 henomenon is reported by the crowd and the true observation is not availab
 le to center for verification of reports. Finally\, we discuss some of our
  preliminary results and research proposal.
LOCATION:BC 229 https://plan.epfl.ch/?room==BC%20229
STATUS:CONFIRMED
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