TY - JOUR
T1 - Task assignment optimization in knowledge-intensive crowdsourcing
AU - Basu Roy, Senjuti
AU - Lykourentzou, Ioanna
AU - Thirumuruganathan, Saravanan
AU - Amer-Yahia, Sihem
AU - Das, Gautam
N1 - Funding Information:
The work of Saravanan Thirumuruganathan and Gautam Das is partially supported by NSF Grants 0812601, 0915834, 1018865, a NHARP grant from the Texas Higher Education Coordinating Board, and grants from Microsoft Research and Nokia Research.
Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2015/8/24
Y1 - 2015/8/24
N2 - We present SmartCrowd, a framework for optimizing task assignment in knowledge-intensive crowdsourcing (KI-C). SmartCrowd distinguishes itself by formulating, for the first time, the problem of worker-to-task assignment in KI-C as an optimization problem, by proposing efficient adaptive algorithms to solve it and by accounting for human factors, such as worker expertise, wage requirements, and availability inside the optimization process. We present rigorous theoretical analyses of the task assignment optimization problem and propose optimal and approximation algorithms with guarantees, which rely on index pre-computation and adaptive maintenance. We perform extensive performance and quality experiments using real and synthetic data to demonstrate that the SmartCrowd approach is necessary to achieve efficient task assignments of high-quality under guaranteed cost budget.
AB - We present SmartCrowd, a framework for optimizing task assignment in knowledge-intensive crowdsourcing (KI-C). SmartCrowd distinguishes itself by formulating, for the first time, the problem of worker-to-task assignment in KI-C as an optimization problem, by proposing efficient adaptive algorithms to solve it and by accounting for human factors, such as worker expertise, wage requirements, and availability inside the optimization process. We present rigorous theoretical analyses of the task assignment optimization problem and propose optimal and approximation algorithms with guarantees, which rely on index pre-computation and adaptive maintenance. We perform extensive performance and quality experiments using real and synthetic data to demonstrate that the SmartCrowd approach is necessary to achieve efficient task assignments of high-quality under guaranteed cost budget.
KW - Collaborative crowdsourcing
KW - Human factors
KW - Knowledge-intensive crowdsourcing
KW - Optimization
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U2 - 10.1007/s00778-015-0385-2
DO - 10.1007/s00778-015-0385-2
M3 - Article
AN - SCOPUS:84937968933
SN - 1066-8888
VL - 24
SP - 467
EP - 491
JO - VLDB Journal
JF - VLDB Journal
IS - 4
ER -