Automatic Mashup Creation Based on Category-Aware Service Clustering

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-33 Number-3
Year of Publication : 2016
Authors : P. Radhika, G. Nisha, K.Nalini, G. Vijayasekaran
  10.14445/22312803/IJCTT-V33P122

MLA

P. Radhika, G. Nisha, K.Nalini, G. Vijayasekaran "Automatic Mashup Creation Based on Category-Aware Service Clustering". International Journal of Computer Trends and Technology (IJCTT) V33(3):105-108, March 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Mashup are a combination of two or more data sources that have been integrated into one service. The number of services are increased in the internet with different category. Since the mashup developers are not clear about select the best service in the internet. However the existing recommendation approach have the list to category the service. Which has deficiencies the meaningless services are ranked. It affect the recommendation approach then the clustering the service not consider the space to stored in database. The proposed system have two algorithm for ranking the services are create the mashup. First kmeansvariant(vkmean) algorithm is used for clustering the service based on collaborative filtering. Second the slicing algorithm is implemented for partition the service and reduce the space for cluster the service. The ranking method is used to rank the service.

References
[1] Al-Masri.E and Q.H. Mahmoud, "Investigating Web Services on the World Wide Web", in Proceedings of The 17th International Conference on World Wide Web (WWW), 2008, Beijing, China, pp. 795-804.
[2] Blei.D.M, Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation", The Journal of Machine Learning Research, 2003, 3: pp. 993-1022.
[3] Do.M.N, and M. Vetterli, "Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback- Leibler Distance", IEEE Transactions on Image Processing, 2002, 11(146- 158).
[4] Huang.G, Q. Zhu, and C. Siew, "Extreme Learning Machine: Theory and Applications", Neurocomputing, 2006, 70: pp. 489- 501.
[5] Huang.K, Y. Fan, and W. Tan, "An Empirical Study of Programmable Web: A Network Analysis on a Service- Mashup System", in Proceedings of IEEE 19th International Conference on Web Services (ICWS), 2012, pp. 552-559.
[6] Leitner.P, W. Hummer, and S. Dustdar, "Cost-Based Optimization of Service Compositions", IEEE Transactions on Services Computing (TSC), 2013, 6: pp. 239-251.
[7] Skoutas.D, D. Sacharidis, A. Simitsis, and T. Sellis, "Ranking and Clustering Web Services Using Multicriteria Dominance Relationships", IEEE Transactions on Services Computing (TSC), 2010, 3: pp. 163-177.
[8] Tang.M, Y. Jiang, J. Liu, and X. Liu, "Location-Aware Collaborative Filtering for QoS-Based Service Recommendation", in Proceedings of IEEE 19th International Conference on Web Services (ICWS), 2012, Honolulu, HI, USA, pp. 202-209.
[9] Yin.H, B. Cui, J. Li, J. Yao, and C. Chen, "Challenging the Long Tail Recommendation", in Proceedings of The 38th International Conference on Very Large Data Bases, Aug. 27-31, 2012, Istanbul, Turkey, pp. 896-907.
[10] Yu.Q, Z. Zeng, and H. Wang, "Trace Norm Regularized Matrix Factorization for Service Recommendation", IEEE 20th International Conference on Web Services (ICWS), 2013, Santa Clara, CA: pp. 34-41.
[11] Zhang.J, W. Tan, J. Alexander, I. Foster, and R. Madduri, "Recommend-As-You-Go: A Novel Approach Supporting Services-Oriented Scientific Workflow Reuse", in Proceedings of IEEE International Conference on Services Computing (SCC), Jul. 4-9, 2011, Washington DC, USA, pp. 48-55.
[12] Zhou.Y, L. Liu, C.-S. Perng, A. Sailer, I. Silva-Lepe, and Z. Su, "Ranking Services by Service Network Structure and Service Attributes", in Proceedings of EEE 20th International Conference on Web Services (ICWS), Santa Clara, CA, pp. 26-33.

Keywords
vkmeans, SCRR, CDRR, Mashup, Slicing algorithm.