Conference proceeding
Reusable Meta-Models for Crowdsourcing Driven Elastic Systems (Invited Paper)
2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), pp 50-57
Jul 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Elastic systems utilize both human and machine working units to accomplish tasks that are eligible for crowdsourcing. The quality in the results of work completed by either type of computing unit is tantamount on the characteristics they bear. In this paper we draw parallels from our previous work into looking at the suitability of working units in completing viable tasks in crowdsourcing. We seek to understand characteristics for modeling tasks and workers within these types of systems. Based on our experiments and lessons learned in related literature, we propose a dynamic worker-task information meta-model with a corresponding operational workflow model that can be used in a variety of problem domains involving crowdsourced tasks to provide support in making this decision.
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Details
- Title
- Reusable Meta-Models for Crowdsourcing Driven Elastic Systems (Invited Paper)
- Creators
- Julian Jarrett - Drexel UniversityM. Brian Blake - Drexel UniversityIEEE
- Publication Details
- 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), pp 50-57
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Web of Science ID
- WOS:000391397100006
- Scopus ID
- 2-s2.0-84991281821
- Other Identifier
- 991019318939604721
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- Web of Science research areas
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic