Share This Article:

Classification of Web Services Using Bayesian Network

Abstract Full-Text HTML Download Download as PDF (Size:272KB) PP. 291-296
DOI: 10.4236/jsea.2012.54034    5,671 Downloads   9,349 Views   Citations

ABSTRACT

In this paper, we employed Na?ve Bayes, Markov blanket and Tabu search to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes are treated as criteria, to classify web services. From the experiments, we conclude that Na?ve based Bayesian network performs better than other two techniques comparable to the classification done in literature.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Mohanty, V. Ravi and M. R. Patra, "Classification of Web Services Using Bayesian Network," Journal of Software Engineering and Applications, Vol. 5 No. 4, 2012, pp. 291-296. doi: 10.4236/jsea.2012.54034.

References

[1] L. Z. Zeng, B. Benatallah, M. Dumas, J. Z. Kalagnanam and Q. Z. Sheng, “Web Engineering: Quality Driven Web Service Composition,” Proceedings of the 12th International Conference on World Wide Web, Budapest, 2003, pp. 411-421. doi:10.1145/775152.775211
[2] A. Tsalgatidou and T. Pilioura, “An Overview of Standards and Related Technology in Web Services,” Distributed and Parallel Database, Vol. 12, No. 2-3, 2002, pp. 135-162. doi:10.1023/A:1016599017660
[3] P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss”, Machine Learning, Vol. 29, No. 2-3, 1997, pp. 103-130. doi:10.1023/A:1007413511361
[4] P. Cheeseman and J. Stutz, “Bayesian Classification (Auto Class): Theory and Results,” Advances in Knowledge Discovery and Data Mining, Vol. 180, 1996, pp. 153180.
[5] D. Margaritis, “Learning Bayesian Network Model Structure,” Technical Report CMU-CS-03-153, 2003.
[6] I. Tsamardinos, C. F. Aliferis and A. Statnikov, “LargeScale Feature Selection Using Markov Blanket Induction for the Prediction of Protein-Drug Binding,” Technical Report DSL-02-08, Vanderbilt University, Nashville, 2002.
[7] I. Tsamardinos, C. Aliferis, and A. Statnikov, “Algorithms for Largescale Local Causal Discovery in the Presence of Small Sample or Large Causal Neighborhood,” Technical Report DSL-02-08, Vanderbilt University, Nashville, 2002.
[8] D. M. Chickering, “Learning Equivalence Classes of Bayesian-Network Structures,” Journal of Machine Learning Research, Vol. 2, 2002, pp. 507-554.
[9] S. Vinoski, “Service Discovery 101,” Proceedings of Internet Computing Conference on IEEE, Vol. 7, No.1, 2003, pp. 69-71. doi:10.1109/MIC.2003.1167342
[10] G. V. Bochmann, B. Kerherve, H. Lutffiyya, M.-V. M. Salem and H. Ye, “Introducing QoS to Electronic Commerce Applications,” Proceedings of the 2nd International Symposium on Electronic Commerce (ISEC), HongKong, 26-28 April, 2001 pp. 138-147.
[11] A. Mani and A. Nagarajan, “Understanding Quality of Services for Web Services,” 2002. http://www-106.ibm.com/developerworks/library/ws-quality
[12] S. Ran, “A Model for Web Services Discovery with QoS,” ACM SIGecom Exchange, Vol. 4, No. 1, 2003. doi:10.1145/844357.844360
[13] M. Conti, E. Gregori and F. Panzieri, “Load Distribution among Replicated Web Services A: QoS Based Approach,” Second Workshop on Internet Server Performance, ACM Press, 1999.
[14] O. Ardaiz, F. Freitag and L. Navarro, “Improving Service Time of Web Clients Using Server Redirection,” ACM SIGMETRICS Performances Evaluation Review, Vol. 29, No. 2, 2001, pp. 39-44. doi:10.1145/572317.572324
[15] S. Kalepu, S. Krishnaswamy and S. W. Loke, “Verity: A QoS Metric for Selecting Web Services and Providers,” Proceedings of the 4th International Conference on Web Mining System Engineering Workshops (WISEW’03), 2004, pp. 131-139. doi:10.1109/WISEW.2003.1286795
[16] S. Araban and L. Sterling, “Measuring Quality of Service for Contract Aware Web-Services,” First Australian Workshop on Engineering Service-Oriented Systems (AWESOS 2004), Melbourne, 29 January 2004, pp. 116-127.
[17] QWS Datasets. http://www.uogue/ph.ca/~qmahmoud/qws/index.html
[18] E. AL-Masri and Q. H. Mahmoud, “Investing Web Services on the World Wide Web,” The 17th International Conference on World Wide Web, Beijing, 21-25 April 2008, pp. 795-804.
[19] E. AL-Masri and Q. H. Mahmoud, “QoS Based Discovery and Ranking of Web Services,” IEEE 16th International Conference on Computer Communications and Networks (ICCCN), Honolulu, 13-16 August 2007, pp. 529-534. doi:10.1109/ICCCN.2007.4317873
[20] J. Pearl, “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference,” Morgan Kaufmann, 1988.
[21] F. Glover and M. Laguna, “Tabu Search,” Kluwer Academic Publishers, Amsterdam, 1997. doi:10.1007/978-1-4615-6089-0
[22] R. Mohanty, V. Ravi and M. R. Patra: “Web Services Classification Using Intelligent Techniques,” Expert Systems with Applications, Vol. 37, No. 7, 2010, pp.54845490. doi:10.1016/j.eswa.2010.02.063

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.