A Framework for Transactional Service Selection Based on Crowdsourcing
The growing number of services in the Web providing the same functionality but different QoS (e.g., price, execution time, and availability) and transactional properties (e.g., compensable or not) has lead to the emergence of several approaches for service selection and recommendation. Some of these approaches use collaborative filtering, QoS prediction, service reputation, among others. Existing works lack from a way to integrate all those methods and benefit from their multiple perspectives to decide how to select a service. The problem tackled in this work is the selection of the most suitable service from a set of functionally equivalent services according to the opinions of multiple contributors. We propose a framework to easily rely on crowdsourcing for service selection, where crowdsourcing contributors can be independently developed services or human experts. Our framework emphasizes on the definition of a collaborative system to allow contributors to join and participate in the selection of services.
KeywordsWeb service Service selection Crowdsourcing QoS Transactional properties Voting systems
Unable to display preview. Download preview PDF.
- 1.Aznag, M., Quafafou, M., Durand, N., Jarir, Z.: Web services discovery and recommendation based on information extraction and symbolic reputation (2013). CoRR, abs/1304.3268Google Scholar
- 2.Cardinale, Y., El Haddad, J., Manouvrier, M., Rukoz, M.: Transactional-aware Web Service Composition: A Survey. IGI Global - Advances in Knowledge Management (AKM) Book Series, ch. 6, pp. 2–20 (2011)Google Scholar
- 3.Cardinale, Y., Rukoz, M.: Fault tolerant execution of transactional compositeweb services: an approach. In: The Fifth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2011, pp. 158–164 (2011)Google Scholar
- 5.Chen, L., Wu, J., Jian, H., Deng, H., Wu, Z.: Instant recommendation for web services composition. IEEE Transactions on Services Comp. 99(PrePrints), 1 (2013)Google Scholar
- 8.Kang, G., Liu, J., Tang, M., et al.: Awsr: active web service recommendation based on usage history. In 2012 IEEE 19th Int. Conf. on Web Services (ICWS), pp. 186–193 (2012)Google Scholar
- 9.Klusch, M., Fries, B., Sycara, K.: Automated semantic web service discovery with OWLS-MX. In: Proceedings of the Fifth Int. Joint Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 915–922. ACM, New York (2006)Google Scholar
- 13.Seber, G.A., Lee, A.J.: Linear regression analysis, vol. 936. John Wiley & Sons (2012)Google Scholar
- 14.Sharifi, M., Manaf, A., Memariani, A., Movahednejad, H., Md Sarkan, H., Dastjerdi, A.: Multi-criteria consensus-based service selection using crowdsourcing. In: 2014 28th Int. Conf. on Advanced Information Networking and Applications Workshops (WAINA), pp. 114–120, May 2014Google Scholar
- 16.Thio, N., Karunasekera, S.: Automatic measurement of a qos metric for web service recommendation. In: Proceedings of the 2005 Australian Soft. Eng. Conf., pp. 202–211, March 2005Google Scholar
- 18.Zheng, Z., Lyu, M.: Collaborative reliability prediction of service-oriented systems. In: 2010 ACM/IEEE 32nd Int. Conf. on Software Engineering, vol. 1, pp. 35–44 (2010)Google Scholar