Task assignment optimization in knowledge-intensive crowdsourcing

Senjuti Basu Roy, Ioanna Lykourentzou, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)467-491
Number of pages25
JournalVLDB Journal
Volume24
Issue number4
DOIs
StatePublished - Aug 24 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Hardware and Architecture

Keywords

  • Collaborative crowdsourcing
  • Human factors
  • Knowledge-intensive crowdsourcing
  • Optimization

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