| Title |
Feature selection and clustering based web service selection using qoSs |
| ID_Doc |
43762 |
| Authors |
Purohit, L; Rathore, SS; Kumar, S |
| Title |
Feature selection and clustering based web service selection using qoSs |
| Year |
2023 |
| Published |
Applied Intelligence, 53, 11 |
| DOI |
10.1007/s10489-022-04042-w |
| Abstract |
Web Services act as a backbone to realize the smart city concept. Web service technology is useful to offer various services as part of the smart city. From the smart city perspective, the fundamental problem is selecting the web services offering desired functionality and meeting an end-user's quality of Service (QoS) expectations. With the rapid increase in the number of web services with similar functionality, the performance of the selection mechanism degrades, and the complexity of the web service selection mechanism increases. A web service selection method is presented in this work, which combines feature selection and QoS-based clustering for an improved web service selection mechanism. The presented method aims to improve the performance and quality of the web service selection mechanism and reduce the complexity. An empirical analysis of the presented method using QoS parameters is performed on the real-world web services QWS dataset, available in the public repository. We compare the performance of the presented method with other state-of-the-art clustering techniques using different evaluation measures based on various performance parameters for the quality of clustering. The experimental results showed that integrating feature selection and QoS-based clustering in the selection mechanism improves the quality of clusters and ultimately improves the performance of the web service selection. |
| Author Keywords |
Web services; Clustering techniques; Web service selection; Feature selection |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000864967800001 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
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