Crowdsourcing analytics with CrowdCur

Authors

Mohammadreza Esfandiari, New Jersey Institute of Technology
Kavan Bharat Patel, New Jersey Institute of Technology
Sihem Amer-Yahia, Laboratoire d'Informatique de Grenoble
Senjuti Basu Roy, New Jersey Institute of Technology

Document Type

Conference Proceeding

Publication Date

5-27-2018

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.

Identifier

85048750759 (Scopus)

ISBN

[9781450317436]

Publication Title

Proceedings of the ACM SIGMOD International Conference on Management of Data

External Full Text Location

https://doi.org/10.1145/3183713.3193563

ISSN

07308078

First Page

1701

Last Page

1704

Recommended Citation

Esfandiari, Mohammadreza; Patel, Kavan Bharat; Amer-Yahia, Sihem; and Roy, Senjuti Basu, "Crowdsourcing analytics with CrowdCur" (2018). Faculty Publications. 8657.
https://digitalcommons.njit.edu/fac_pubs/8657