Abstract
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).
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References
Alonso, G., Casati, F., Kuno, H., Machiraju, V.: Web services. In: Web Services: Concepts, Architectures and Applications, pp. 123–149. Springer, Heidelberg (2004)
Tian, G., Liu, P., Peng, Y., Sun, C.: Tagging augmented neural topic model for semantic sparse web service discovery. Concurr. Comput. 30(16), 1–10 (2018)
Hayyolalam, V., Pourhaji Kazem, A.A.: A systematic literature review on QoS-aware service composition and selection in cloud environment. J. Netw. Comput. Appl. 110, 52–74 (2018)
Chifu, V.R., Pop, C.B., Salomie, I., Chifu, E.S.: Hybrid honey bees mating optimization algorithm for identifying the near-optimal solution in web service composition. Comput. Inf. 36(5), 1143–1172 (2017)
Sellami, M., Bouchaala, O., Gaaloul, W., Tata, S.: Communities of web service registries: construction and management. J. Syst. Softw. 86(3), 835–853 (2013)
Cao, B., Frank Liu, X., Liu, J., Tang, M.: Domain-aware Mashup service clustering based on LDA topic model from multiple data sources. Inf. Softw. Technol. 90, 40–54 (2017)
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)
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)
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)
Chen, F., Li, M., Wu, H., Xie, L.: Web service discovery among large service pools utilising semantic similarity and clustering. Enterp. Inf. Syst. 11(3), 452–469 (2017)
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)
Nanda, S.J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)
Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Appl. Soft Comput. J. 11(2), 1574–1587 (2011)
Bereta, M., Burczyński, T.: Immune K-means and negative selection algorithms for data analysis. Inf. Sci. (Ny) 179(10), 1407–1425 (2009)
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)
Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Ann. Data Sci. 2(2), 165–193 (2015)
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)
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)
Acknowledgements
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.
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Garba, S., Mohamad, R., Saadon, N.A. (2020). Search Space Reduction Approach for Self-adaptive Web Service Discovery in Dynamic Mobile Environment. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_104
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