Experimental Analysis for Semantic Based Large Scale Service Composition using Deep Learning
Jackulin Sam Gini. A1, A. Chandrasekar2
1Jackulin Sam Gini.A, Research Scholar, Sathyabama Institute of Science and Technology, Chennai-600 119, India
2A. Chandrasekar, Professor& Head, Department of Computer Science Engineering, St. Joseph’s College of Engineering, Old Mamallapuram Road, Chennai-600 119, India
Manuscript received on 01 August 2019 | Revised Manuscript received on 05 August 2019 | Manuscript published on 30 August 2019 | PP: 4280-4283 | Volume-8 Issue-10, August 2019 | Retrieval Number: J10610881019/19©BEIESP | DOI: 10.35940/ijitee.J1061.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Service Oriented Architecture (SOA) web services plays important role. Web services are web application components that can be published, found, and used on the Web. Also machine-to-machine communication over a network can be achieved through web services. Cloud computing and distributed computing brings lot of web services into WWW. Web service composition is the process of combing two or more web services to together to satisfy the user requirements. Tremendous increase in the number of services and the complexity in user requirement specification make web service composition as challenging task. The automated service composition is a technique in which Web Service Composition can be done automatically with minimal or no human intervention. In this paper we propose a approach of web service composition methods for large scale environment by considering the QoS Parameters. We have used stacked autoencoders to learn features of web services. Recurrent Neural Network (RNN) leverages uses the learned features to predict the new composition. Experiment results show the efficiency and scalability. Use of deep learning algorithm in web service composition, leads to high success rate and less computational cost.
Keywords: Web Services, Web Service Composition, Semantic Web, Deep Learning.
Scope of the Article: Deep Learning