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QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms

QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms

Priya Bhaskar Pandharbale, Sachi Nandan Mohanty, Alok Kumar Jagadev
Copyright: © 2022 |Volume: 13 |Issue: 6 |Pages: 16
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781668458174|DOI: 10.4018/IJISMD.301274
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MLA

Pandharbale, Priya Bhaskar, et al. "QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms." IJISMD vol.13, no.6 2022: pp.1-16. http://doi.org/10.4018/IJISMD.301274

APA

Pandharbale, P. B., Mohanty, S. N., & Jagadev, A. K. (2022). QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms. International Journal of Information System Modeling and Design (IJISMD), 13(6), 1-16. http://doi.org/10.4018/IJISMD.301274

Chicago

Pandharbale, Priya Bhaskar, Sachi Nandan Mohanty, and Alok Kumar Jagadev. "QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms," International Journal of Information System Modeling and Design (IJISMD) 13, no.6: 1-16. http://doi.org/10.4018/IJISMD.301274

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Abstract

Service-oriented computing (SOC) activates communication through web services to provide computing as a service for business applications in the service-oriented architecture (SOA). To make SOC successful, finding a needed service to build a system directly depending on the collection of services is a critical confront. In this paper, the authors planned the clustering-based approach called dynamic clustering (DCLUS). The novelty in DCLUS compared to static-based clustering technique is the use of dynamic clustering technique. In existing CLUS, the static various widths clustering method is exploited for the users and services clustering. However, due to the limitations of static clustering, they proposed dynamic clustering to optimize the performance of clustering using data mining to find the associations and patterns, for services, and also the prediction accuracy. The performance of the proposed DCLUS system will be implemented and evaluated facing the existing system in phases of precision, recall, and f-score performance metrics using the research dataset.