Abstract
Service composition is one of the main behavior in the SOC(Service-Oriented Computing) process, which direct and indirect influences effectiveness and precision of service computing; But at present, relation researches mainly focus on semantics recognition and QoS(Quality of Service). In the paper, according to semantics characteristic classification, we proposed a semantics web service characteristic composition approach based on particle swarm optimization, and set up a characteristic selsection mechanism of semantics web service, and adopt charecteristic distance relation to implement service characteristic classification, and use the distance relation to build characteristic tendency degree, sufficiency and characteristic extractor computing formula of semantics web service, at the same time, according to the formula, to implement service characteristic composition algorithm. Then, we set up a optimal mathematical model via characteristic extractor formula. And employ particle swarm to optimize the model and Amazon service set to make experiment, which showed that it is feasible and effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blessa, P.N., Klabjan, D., Chang, S.Y.: Heuristics for automated knowledge source integration and service composition. Computers & Operations Research 35, 1292–1314 (2008)
Liang, W.-Y., Huang, C.-C.: The generic genetic algorithm incorporates with rough set theory - An application of the web services composition. Expert Systems with Applications 36(3), 5549–5556 (2009)
Wang, J.S., Li, Z.J., Li, M.J.: Compose semantic web services with description logics. Journal of Software 19(4), 957–970 (2008)
Myoung, J., Ouk, C., Ick-Hyun: Quality-of-service oriented web service composition algorithm and planning architecture. Journal of Systems and Software 81(11), 2079–2090 (2008)
Ai, W.-h., Song, Z.-l., Wei, L., Wu, L.: Web Service Discovery Based on Domain Ontology. Journal of University of Electronic Science and Technology of China 36(3), 506–509 (2007)
Muhammad Ahsan, S.: A framework for QoS computation in web service and technology selection. Computer Standards & Interfaces 28, 714–720 (2006)
Qiang, X., Lei, Z., Liang, Z.: Ontology Partition Method Based on ImprovedParticle Swarm Optimization Algorithm. Journal of South China University of Technology (Natural Science Edition) 35(9), 118–122 (2007)
Patil, A., Oundhakar, S., Sheth, A., et al.: Meteor-S Web Service Annotation Framework 2008. In: Proc. of the 13th International Conference on World Wide Web, pp. 17–22. ACM Press, New York (2004)
Bian, S., Zhang, X.: Pattern recognition, 2nd edn. Tsinghua University Press (2001)
Xu, M., Chen, J.L., Peng, Y., Mei, X.: Service relationship ontology-based Web services creation. Journal of Software 19(3), 545–556 (2008)
Xiangbing, Z.: Semantic Web Services Component Automata Based Ontology 2008. In: Proceedings of the 27th Chinese Control Conference, Kunming, Yunnan, China, pp. 719–723. IEEE Press, Los Alamitos (2008)
Eberhart Russell, C., Yuhui, S.: Comparison between genetic algorithms and particle swami optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)
Ji, Z., Liao, H., Wu, Q.: Particle Swarm Optimization and Application. Science Press (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Xiangbing, Z. (2010). Semantics Web Service Characteristic Composition Approach Based on Particle Swarm Optimization. In: Luo, Q. (eds) Advancing Computing, Communication, Control and Management. Lecture Notes in Electrical Engineering, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05173-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-05173-9_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05172-2
Online ISBN: 978-3-642-05173-9
eBook Packages: Computer ScienceComputer Science (R0)