Analysing the characteristics of crowdsourcing platforms for improving throughput

被引：0
作者：
Kurup A.R. [1 ]
Sajeev G.P. [1 ]
机构：
[1] Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri
来源：
International Journal of Web Engineering and Technology | 2019年 / 14卷 / 03期
关键词：
Behavioural analysis; Crowdsourcing; Probability distribution; Workload generation;
D O I：
10.1504/IJWET.2019.105588
中图分类号：
学科分类号：
摘要：
Crowdsourcing leverages human intelligence to gather solutions on tasks that cannot be accomplished by automated tools. This system consists of components such as the requester, task, worker and the crowdsourcing platform. Studies do not explore the various features of these components and the dependencies among the same. Hence, we analyse the characteristics of the components of crowdsourcing systems using a trace-driven approach. Additionally, for reproducible research, we have introduced a workload generator for crowdsourcing platforms, which generates an unbiased workload similar to the empirical workload. Finally, the impact of various characteristics on the quality of answers has been analysed using both the empirical and synthetic workloads. The results demonstrate that success rate and activeness positively affect the productivity of workers, while the number of available human intelligence tasks (HITs) and the time duration of the same affect the productivity on each task. Copyright © 2019 Inderscience Enterprises Ltd.
引用
收藏
页码：255 / 279
页数：24
相关论文
共 30 条
  • [1] Abirami K., Harini N., Vaidhyesh P.S., Kumar P., Analysis of web workload on QoS to assist capacity, International Conference on ISMAC in Computational Vision and Bio-Engineering, pp. 573-582, (2018)
  • [2] Bai G., Williamson C., Time-domain analysis of web cache filter effects, Performance Evaluation, 58, 2-3, pp. 285-317, (2004)
  • [3] Brabham D.C., Crowdsourcing, (2013)
  • [4] Chandrasekaran V., Rajan S.V., Vasani R.K., Menon A., Sivakumar P.B., Velayutham C.S., A crowdsourcing-based platform for better governance, Proceedings of the International Conference on Soft Computing Systems, pp. 519-527, (2016)
  • [5] CrowdFlower
  • [6] Czwajda L., Kosacka-Olejnik M., Kudelska I., Kostrzewski M., Sethanan K., Pitakaso R., Application of prediction markets phenomenon as decision support instrument in vehicle recycling sector, LogForum, 15, 2, pp. 265-278, (2019)
  • [7] Daniel F., Kucherbaev P., Cappiello C., Benatallah B., Allahbakhsh M., Quality control in crowdsourcing: A survey of quality attributes, assessment techniques, and assurance actions, ACM Computing Surveys (CSUR), 51, 1, (2018)
  • [8] Difallah D., Filatova E., Ipeirotis P., Demographics and dynamics of mechanical Turk workers, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 135-143, (2018)
  • [9] Difallah D.E., Catasta M., Demartini G., Ipeirotis P.G., Cudr e-Mauroux P., The dynamics of micro-task crowdsourcing: The case of Amazon MTURK, Proceedings of the 24th International Conference on World Wide Web, pp. 238-247, (2015)
  • [10] Estelles-Arolas E., Gonzalez-Ladron-De-Guevara F., Towards an integrated crowdsourcing definition, Journal of Information Science, 38, 2, pp. 189-200, (2012)
123