A Powerful Web Benefit Positioning Strategy by Means of Investigating Client Conduct
-
2018-12-13 https://doi.org/10.14419/ijet.v7i4.39.27727 -
Net benefit, positioning strategy, utilitarian significance, communitarian sifting, QoS significance, client conduct. -
Abstract
Administration situated figuring and Web administrations are winding up increasingly prominent, empowering associations to utilize the Net for business opportunity offering Net benefits & expanding current Net administrations. By the by, through expanding reception & nearness of Net administrations, moves toward becoming more hard to locate the most proper Web benefit that fulfills the two clients' useful and nonfunctional necessities. In this paper, we propose a powerful Web benefit positioning methodology in view of communitarian sifting (CS) by investigating the client conduct, where summon and question past records is utilized to construe the probable client conduct. CS-rooted client similitude ascertained through comparative summons and comparative inquiries (counting useful question and QoS inquiry) between clients. Three angles of Web administrations—useful significance, CS rooted outcome, and QoS utility, altogether contemplate in last Web benefit positioning. Dodging effect various components, scale, & dispersion of factors, 3 positions is ascertained the 3 measures separately. Last Web benefit positioning gotten through utilizing a score accumulation strategy dependent through score stand. The paper likewise propound compelling assessment measurements for assess the methodology. Substantial parameter tests were directed dependent to the certifiable Net benefit data subdivision. Exploratory outcomes demonstrate that the proposed methodology outflanks the current methodology on the rank execution.
Â
Â
-
References
- [1] Birukou et al., “Improving web service discovery with usage data,†IEEE Softw., vol. 24, no. 6, pp. 47–54, Nov. /Dec. 2007.
[2] D. Kourtesis and I. Paraskakis, “Combining SAWSDL, OWL-DL and UDDI for semantically enhanced web service discovery,†in The Semantic Web: Research and Applications. New York, NY, USA: Springer, 2008, pp. 614–628.
[3] Y. Zhang, Z. Zheng, and M. R. Lyu, “WSExpress: A QoS-aware search engine for web services,†in Proc. Int. Conf. Web Serv., 2010, pp. 91–98
[4] G. Kang et al., “An effective dynamic web service selection strategy with global optimal QoS based on particle swarm optimization algorithm,†in Proc. IEEE 26th Int. Parallel Distrib. Process. Symp. Workshops PhD Forum (IPDPSW), 2012, pp. 2274–2279
[5] G. Kang et al., “Web service selection for resolving conflicting service requests,†in Proc. Int. Conf. Web Serv., 2011, pp. 387–394.
[6] N. Hiratsuka, F. Ishikawa, and S. Honiden, “Service selection with combinational use of functionally-equivalent services,†in Proc. IEEE Int. Conf.Web Serv., 2011, pp. 97–104.
[7] W. Dou et al., “A QoS-aware service evaluation method for co-selection a shared service,†in Proc. IEEE Int. Conf. Web Serv., 2011, pp. 145–152.
[8] G. Kang et al., “Web service selection algorithm based on principal component analysis,†J. Electron. (China), vol. 30, no. 2, pp. 1–9, 2012.
[9] L. Yao et al., “Recommending web services via combining collaborative filtering with content-based features,†in Proc. IEEE Int. Conf. Web Serv., 2013, pp. 42–49.
[10] Q. Zhang, C. Ding, and C. H. Chi, “Collaborative filtering based service ranking using invocation histories,†in Proc. IEEE Int. Conf. Web Serv., 2011, pp. 195–202.
[11] L. Qi et al., “Combining local optimization and enumeration for QoSaware Web service composition,†in Proc. IEEE Int. Conf. Web Serv., 2010, pp. 34–41.
[12] M. Alrifai, D. Skoutas, and T. Risse, “Selecting skyline services for QoSbased web service composition,†in Proc. Int. World Wide Web Conf., 2010, pp. 11–20.
[13] S. S. Yau and Y. Yin, “QoS-based service ranking and selection for service-based systems,†in Proc. Int. Conf. Serv. Comput., 2011, pp. 56–63.
[14] L. Shao et al., “Personalized QoS prediction for web services via collaborative filtering,†in Proc. IEEE Int. Conf. Web Serv., 2007, pp. 439–446.
[15] Z. Zheng et al., “WSRec: A collaborative filtering based web service recommender system,†in Proc. IEEE Int. Conf. Web Serv., 2009, pp. 437–444.
[16] Y. Jiang et al., “An effective Web service recommendation based on personalized collaborative filtering,†in Proc. IEEE Int. Conf. Web Serv., 2011, pp. 211–218.
[17] X. Chen et al., “RegionKNN: A scalable hybrid collaborative filtering algorithm for personalized Web servicer recommendation,†in Proc. IEEE Int. Conf. Web Serv., 2010, pp. 9–16.
[18] M. Tang et al., “Location-aware collaborative filtering for QoS-based service recommendation,†in Proc. IEEE Int. Conf. Web Serv., 2012, pp. 202–209.
[19] W. Lo et al., “Collaborative web service QoS prediction with location based regularization,†in Proc. IEEE Int. Conf. Web Serv. (ICWS), 2012, pp. 464–471.
[20] J.Wu et al., “Predicting quality of service for selection by neighbourhood based collaborative filtering,†IEEE Trans. Syst. Man Cybern. Syst., vol. 43, no. 2, pp. 428–439, Mar. 2013.
[21] Y. Xu et al., “Personalized location-aware QoS prediction for web services using probabilistic matrix factorization,†in Proc.Web Inf. Syst. Eng. (WISE), 2013, pp. 229–242.
[22] Z. Zheng et al., “Collaborative Web service QoS prediction via neighbourhood integrated matrix factorization,†IEEE Trans. Serv. Comput., vol. 6, no. 3, pp. 289–299, Jul./Sep. 2013.
[23] P. He et al., “Location-based hierarchical matrix factorization for web service recommendation,†in Proc. IEEE Int. Conf. Web Serv., 2014, pp. 297–304.
[24] G. Kang et al., “Diversifying web service recommendation results via exploring service usage history,†IEEE Trans. Serv. Comput., vol. 6, no. 1,pp. 35–47, 2015.
[25] M. Klusch, B. Fries, and K. Sycara, “Automated semantic web service discovery with OWLS-MX,†in Proc. Auton. Agents Multiagent Syst., 2006, pp. 915–922.
[26] L. Chen et al., “WT-LDA: User tagging augmented LDA for web service clustering,†in Proc. Int. Conf. Serv. Oriented Comput., 2013, pp. 162–176.
[27] J. Wu et al., “Clustering web services to facilitate service discovery,â€Knowl. Inf. Syst., vol. 38, no. 1, pp. 207–229, 2014.
[28] C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge, U.K.: Cambridge Univ. Press, 2008, vol 1.
[29] C. Dwork et al., “Rank aggregation methods for the web,†in Proc. 10th Int. Conf. World Wide Web, 2001, pp. 613–622.
[30] Y. Liu, A. H. Ngu, and L. Z. Zeng, “QoS computation and policing in dynamic web service selection,†in Proc. Int. World Wide Web Conf., 2004, pp. 66–73.
[31] K. Järvelin and J. Kekäläinen, “Cumulated gain-based evaluation of IR techniques,†ACM Trans. Inf. Syst. (TOIS), vol. 20, no. 4, pp. 422–446, 2002.
[32] Z. Zheng et al., “QoS-aware web service recommendation by collaborative filtering,†IEEE Trans. Serv. Comput., vol. 4, no. 2, pp. 140–152, 2011.
- [1] Birukou et al., “Improving web service discovery with usage data,†IEEE Softw., vol. 24, no. 6, pp. 47–54, Nov. /Dec. 2007.
-
Downloads
-
How to Cite
Gupta, S., Tyagi, N., kr. Saraswat, K., Pratap Srivastava, A., & Awasthi, S. (2018). A Powerful Web Benefit Positioning Strategy by Means of Investigating Client Conduct. International Journal of Engineering & Technology, 7(4.39), 907-914. https://doi.org/10.14419/ijet.v7i4.39.27727Received date: 2019-02-21
Accepted date: 2019-02-21
Published date: 2018-12-13