Challenges in Data Crowdsourcing

Hector Garcia-Molina, Manas Joglekar, Adam Marcus, Aditya Parameswaran, Vasilis Verroios

Research output: Contribution to journalReview article

Abstract

Crowdsourcing refers to solving large problems by involving human workers that solve component sub-problems or tasks. In data crowdsourcing, the problem involves data acquisition, management, and analysis. In this paper, we provide an overview of data crowdsourcing, giving examples of problems that the authors have tackled, and presenting the key design steps involved in implementing a crowdsourced solution. We also discuss some of the open challenges that remain to be solved.

Original languageEnglish (US)
Article number7384520
Pages (from-to)901-911
Number of pages11
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number4
DOIs
StatePublished - Apr 1 2016

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Keywords

  • Data crowdsourcing
  • crowdsourcing design
  • crowdsourcing space
  • crowdsourcing workflow
  • data augmenting
  • data curation
  • data processing
  • worker management

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Garcia-Molina, H., Joglekar, M., Marcus, A., Parameswaran, A., & Verroios, V. (2016). Challenges in Data Crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, 28(4), 901-911. [7384520]. https://doi.org/10.1109/TKDE.2016.2518669