Crowdsourcing analytics with CrowdCur

Mohammadreza Esfandiari, Kavan Bharat Patel, Sihem Amer-Yahia, Senjuti Basu Roy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose to demonstrate CrowdCur, a system that allows platform administrators, requesters and workers to conduct various analytics of interest. CrowdCur includes a worker curation component that relies on explicit feedback elicitation to best capture workers' preferences, a task curation component that monitors task completion and aggregates their statistics, and an OLAP-style component to query and combine analytics by worker, by task type, etc. Administrators can fine tune their system's performance. Requesters can compare platforms and better choose the set of workers to target. Workers can compare themselves to others and find tasks and requesters that suit them best.

Original languageEnglish (US)
Title of host publicationSIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data
EditorsGautam Das, Christopher Jermaine, Ahmed Eldawy, Philip Bernstein
PublisherAssociation for Computing Machinery
Pages1701-1704
Number of pages4
ISBN (Electronic)9781450317436
DOIs
StatePublished - May 27 2018
Event44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018 - Houston, United States
Duration: Jun 10 2018Jun 15 2018

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
CountryUnited States
CityHouston
Period6/10/186/15/18

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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