Search Space Reduction Approach for Self-adaptive Web Service Discovery in Dynamic Mobile Environment
The proliferation of functionally similar Mobile Web Service (MWS) result in huge search space, the discovery of MWS on such large space increases the response time and probability of discovering irrelevant MWS irrespective of the matchmaking algorithm. The existing research on MWS discovery mostly focused on applying coarse-grained search space reduction that fails to deal with cold-start and data sparsity challenges at the expense of large computing resources. The proposed search space reduction is achieved by subsuming k-means in the modified negative selection algorithm (M-NSA) to place the service in an appropriate category so that the matching is only performed on the MWS in the target category. The experimental results show significant improvement in terms of accuracy of the categorization which can improve the MWS discovery in in a dynamic mobile environment (DME).
KeywordsMobile Web Service Discovery Search space reduction Categorization algorithm
We would like to thank the Ministry of Education (MOE) Malaysia for sponsoring the research through the Fundamental Research Grant Scheme (FRGS) with vote number 5F080 and Universiti Teknologi Malaysia for providing the facilities and supporting the research. In addition, we would like to extend our gratitude to the lab members in the EReTSEL Lab, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia for their invaluable ideas and support throughout this study.
- 1.Alonso, G., Casati, F., Kuno, H., Machiraju, V.: Web services. In: Web Services: Concepts, Architectures and Applications, pp. 123–149. Springer, Heidelberg (2004)Google Scholar
- 7.Jiang, B., Ye, L., Wang, J., Wang, Y.: A semantic-based approach to service clustering from service documents. In: 2017 IEEE International Conference on Services Computing (SCC), pp. 265–272 (2017)Google Scholar
- 8.Shi, M., Liu, J., Cao, B., Wen, Y., Zhang, X.: A prior knowledge based approach to improving accuracy of web services clustering. In: 2018 IEEE International Conference on Services Computing (SCC), pp. 1–8 (2018)Google Scholar
- 9.Rupasingha, R.A.H.M., Paik, I., Kumara, B.T.G.S.: Improving web service clustering through a novel ontology generation method by domain specificity. In: Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017, pp. 744–751 (2017)Google Scholar
- 11.Kotekar, S., Kamath, S.S.: Enhancing web service discovery using meta-heuristic CSO and PCA based clustering. Prog. Intell. Comput. Tech. Theory Pract. Appl. 519, 393–403 (2018)Google Scholar
- 15.Li, Z., Tan, H.-Z.: A combinational clustering method based on artificial immune system and support vector machine. In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 153–162 (2006)Google Scholar
- 17.Ahmed, M.S., Khan, L.: SISC: a text classification approach using semi supervised subspace clustering. In: ICDM Workshop 2009 - IEEE International Conference on Data Mining, pp. 1–6 (2009)Google Scholar
- 18.Shi, M., Liu, J., Cao, B., Wen, Y., Zhang, X.: A prior knowledge based approach to improving accuracy of web services clustering. In: 2018 IEEE International Conference on Services Computing, pp. 1–8 (2018)Google Scholar