TY - GEN
T1 - Uncertainty in crowd data sourcing under structural constraints
AU - Amarilli, Antoine
AU - Amsterdamer, Yael
AU - Milo, Tova
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84958550980
U2 - 10.1007/978-3-662-43984-5_27
DO - 10.1007/978-3-662-43984-5_27
M3 - Conference contribution
AN - SCOPUS:84958550980
SN - 9783662439838
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 351
EP - 359
BT - Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, International Workshops
PB - Springer Verlag
T2 - 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014
Y2 - 21 April 2014 through 24 April 2014
ER -