Evaluation of worker quality in crowdsourcing system on Hadoop platform Online publication date: Fri, 24-May-2019
by C. Kavitha; R. Srividhya Lakshmi; J. Anjana Devi; U. Pradheeba
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 11, No. 2, 2019
Abstract: Crowdsourcing is a new emerging distributed computing and problem solving production model on the backdrop of internet. The data size of crowdsources and tasks grows rapidly due to the rapid development of the crowdsourcing system. To evaluate the worker quality, based on the big data technology has become a more complex challenge. In this paper, we propose a general worker quality evaluation algorithm which can be applied to any critical tasks without wasting resources. Realising the evaluation algorithm in the Hadoop platform using MapReduce parallel programming is also involved. Efficiency and accuracy of the algorithm is effectively verified in the wide variety of many big data scenarios.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com