Uncertainty in crowd data sourcing under structural constraints

Antoine Amarilli, Yael Amsterdamer, Tova Milo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are expected to minimize this uncertainty relative to the overall data collection goal. Such problems are already challenging when we assume that questions are unrelated and answers are independent, but they are even more complicated when we assume that the unknown values follow hard structural constraints (such as monotonicity). In this vision paper, we examine how to formally address this issue with an approach inspired by [2]. We describe a generalized setting where we model constraints as linear inequalities, and use them to guide the choice of crowd questions and the processing of answers. We present the main challenges arising in this setting, and propose directions to solve them.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 19th International Conference, DASFAA 2014, International Workshops
Subtitle of host publicationBDMA, DaMEN, SIM3, UnCrowd, Revised Selected Papers
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783662439838
StatePublished - 2014
Externally publishedYes
Event19th International Conference on Database Systems for Advanced Applications, DASFAA 2014 - Bali, Indonesia
Duration: 21 Apr 201424 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8505 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Database Systems for Advanced Applications, DASFAA 2014

Bibliographical note

Funding Information:
This work has been partially funded by the European Research Council under the FP7, ERC grant MoDaS, agreement 291071, and by the Israel Ministry of Science.


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