Abstract
Crowdsourcing is an emerging business model which organizes distributed crowds to solve various problems through the Internet. Recently this paradigm has also flourished in software engineering domain. Despite benefits like on-demand workforce and rich expertise, a major challenge is how to discover cost-effective workers while guarantee high quality of deliveries. Current approaches to worker discovery have primarily based on workers’ interest and self-evaluation which have subjective deviations. In this paper, we present a novel approach to describe each skill on the basis of feedback evaluation and estimate skill evolution dynamically. On the other side, to deal with the growing number of workforce, we use a clustering-based method to group workers with similar characteristics together to reduce search space. Our experiments show the effectiveness and efficiency of this approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)
Brabham, D.C.: Crowdsourcing as a model for problem solving an introduction and cases. Converg. Int. J. Res. N. Media Technol. 14(1), 75–90 (2008)
Kittur, A., Nickerson, J.V., Bernstein, M., et al.: The future of crowd work. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1301–1318, ACM (2013)
Dolstra, E., Vliegendhart, R., Pouwelse, J.: Crowdsourcing gui tests. In: 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation (ICST), pp. 332–341, IEEE (2013)
Musson, R., Richards, J., Fisher, D., et al.: Leveraging the crowd: how 48,000 users helped improve Lync performance. IEEE Softw. 30(4), 38–45 (2013)
Lam, X.N., Vu, T., Le, T.D., et al.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 208–211, ACM (2008)
Le, V.T., Zhang, J., Johnstone, M., et al.: Dynamic control of skilled and unskilled labour task assignments. In: 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 955–960, IEEE (2013)
Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 39–46. ACM, New York, NY, USA (2007)
Xin, X., King, I., Deng, H., Lyu, M.R.: A social recommendation framework based on multi-scale continuous conditional random fields. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1247–1256, New York, NY, USA (2009)
Zhou, T.C., Ma, H., King, I., Lyu, M.R.: Tagrec: leveraging tagging wisdom for recommendation. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 04, pp. 194–199. IEEE Computer Society, Washington, DC, USA (2009)
Yuen, M.C., King, I., Leung, K.S.: Task matching in crowdsourcing. In: 2011 International Conference on and 4th International Conference on Cyber, Physical and Social Computing Internet of Things (iThings/CPSCom), pp. 409–412, IEEE (2011)
Yue, D., Yu, G., Shen, D., et al.: A weighted aggregation rule in crowdsourcing systems for high result accuracy. In: 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 265–270, IEEE (2014)
Cao, C.C., She, J., Tong, Y., et al.: Whom to ask?: jury selection for decision making tasks on micro-blog services. Proc. VLDB Endow. 5(11), 1495–1506 (2012)
Kulkarni, A., Gutheim, P., Narula, P., et al.: Mobileworks: designing for quality in a managed crowdsourcing architecture. IEEE Internet Comput. 16(5), 28–35 (2012)
Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Commun. ACM 54(4), 86–96 (2011)
Yan, S., Zheng, X., Chen, D.: Dynamic service selection with reputation management. In: 2010 International Conference on Service Sciences (ICSS), pp. 9–16, IEEE (2010)
Wang, Y., Vassileva, J.: A review on trust and reputation for web service selection. In: 27th International Conference on Distributed Computing Systems Workshops 2007, ICDCSW 2007, pp. 25–25, IEEE (2007)
Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)
Esteves, R.M., Pais, R., Rong, C.: K-means clustering in the cloud–a Mahout test. In: 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA), pp. 514–519, IEEE (2011)
Acknowledgements
This paper is supported by the Program of University-Industry Cooperation of Shanghai under Granted No. Hu-CXY-2014-013 and the National Natural Science Foundation of China under Granted No. 61472242. This support is gratefully acknowledged. We would also like to express our sincere thanks to anonymous referees, whose comments helped clarify a number of issues.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Song, F., Chen, H., Fu, Y. (2015). An Approach to Rapid Worker Discovery in Software Crowdsourcing. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-27119-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27118-7
Online ISBN: 978-3-319-27119-4
eBook Packages: Computer ScienceComputer Science (R0)