Conference proceeding
Interoperability and Scalability for Worker-Job Matching across Crowdsourcing Platforms
2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp 3-8
Jun 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Crowdsourcing labor market platforms consist of a variety of jobs spanning multiple problem domains and their respective specialized or diverse worker pools. Each platform currently operates independently and isolated from the potential benefits of sharing job and worker pool data across platforms. Previous work introduces infrastructure that optimizes the sharing of both job and worker data collectively, called the open push-pull model. In this paper, to support automated recommendation of workers, we introduce an interoperability standard and computational method that facilitates the aggregation of job data while supporting scalability in response to increasing volumes of data. (i.e. workers and jobs continuously entering the system).
Metrics
Details
- Title
- Interoperability and Scalability for Worker-Job Matching across Crowdsourcing Platforms
- Creators
- Julian Jarrett - Drexel UniversityM. Brian Blake - Drexel University
- Publication Details
- 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp 3-8
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Web of Science ID
- WOS:000426913000003
- Scopus ID
- 2-s2.0-85034270727
- Other Identifier
- 991019319070704721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods