Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/351680
Title: | An Integrated Framework For Dynamic Composition Of Qos Driven Semantic Web Services Using Machine Learning Model |
Researcher: | Sethuraman,R |
Guide(s): | Sasiprabha,T |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Sathyabama Institute of Science and Technology |
Completed Date: | 2021 |
Abstract: | In recent years almost all enterprises distribute and release their products via web services, which can be accessed easily by users. The fast and abundant growth of web services leads anomaly to the users resulting in unreliable and forced to select the services which are not completely satisfying the user request. In general, a single service does not meet complex user requirements, so it is necessary to combine multiple services in order to meet the requirements of the user request and to satisfy the intended user. The web service composition framework has the overall responsibility for the composition Process. The idea behind the web service composition is that allowing the combination of existing services published on the web into a new service with certain high-level functionality imbibed into it and satisfy the business goal. During the process of composition, many services with the same functional attributes, Quality of Service (QoS), also known as non-functional requirements, play a huge challenge in selecting among the competing services. QoS-aware service composition has become a principal research direction that enables the QoS of the resulting composite service is maximized. Due to an increase in complex user requirements, the availability of multiple services for a single user request creates the need for an efficient composition framework for handling the large scale environment. To manage the abundant development of web services, there is a need to convert into a large scale approach rather than the traditional methods considered during the composition of services. To address the above challenges in the dynamic environment, for handling multiple web services with the required QoS, a novel semantic web service composition framework using Machine Learning (ML) model is necessary and is proposed in this work. For a user newline newline newline newline newline newlinerequest, the semantic web service discovery engine discovers a set of all semantically matched services from the service registry us |
Pagination: | A5 |
URI: | http://hdl.handle.net/10603/351680 |
Appears in Departments: | COMPUTER SCIENCE DEPARTMENT |
Files in This Item:
File | Description | Size | Format | |
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01. title.pdf | Attached File | 156.43 kB | Adobe PDF | View/Open |
02. certificate.pdf | 910.51 kB | Adobe PDF | View/Open | |
03. acknowledgement.pdf | 315.91 kB | Adobe PDF | View/Open | |
04. abstract.pdf | 9.58 kB | Adobe PDF | View/Open | |
05. table of contents.pdf | 347.7 kB | Adobe PDF | View/Open | |
06. chapter 1.pdf | 11.91 MB | Adobe PDF | View/Open | |
07. chapter 2.pdf | 2.34 MB | Adobe PDF | View/Open | |
08. chapter 3.pdf | 20.77 MB | Adobe PDF | View/Open | |
09. chapter 4.pdf | 9.17 MB | Adobe PDF | View/Open | |
10. conclusion.pdf | 423.74 kB | Adobe PDF | View/Open | |
11. references.pdf | 5.44 MB | Adobe PDF | View/Open | |
12. curriculam vitae.pdf | 326.69 kB | Adobe PDF | View/Open | |
13. evaluation reports.pdf | 4.26 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 156.43 kB | Adobe PDF | View/Open |
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