The VLDB Journal

, Volume 24, Issue 4, pp 467–491 | Cite as

Task assignment optimization in knowledge-intensive crowdsourcing

  • Senjuti Basu Roy
  • Ioanna Lykourentzou
  • Saravanan Thirumuruganathan
  • Sihem Amer-Yahia
  • Gautam Das
Regular Paper

Abstract

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.

Keywords

Collaborative crowdsourcing Optimization Knowledge-intensive crowdsourcing Human factors 

Supplementary material

778_2015_385_MOESM1_ESM.pdf (167 kb)
Supplementary material 1 (pdf 166 KB)
778_2015_385_MOESM2_ESM.pdf (126 kb)
Supplementary material 2 (pdf 126 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Senjuti Basu Roy
    • 1
  • Ioanna Lykourentzou
    • 2
  • Saravanan Thirumuruganathan
    • 3
  • Sihem Amer-Yahia
    • 4
  • Gautam Das
    • 3
  1. 1.University of Washington TacomaTacomaUSA
  2. 2.CRP Henri Tudor/INRIA Nancy Grand-EstVillers-lés-NancyFrance
  3. 3.UT ArlingtonArlingtonUSA
  4. 4.LIGCNRSGrenobleFrance

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