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Feature selection and clustering based web service selection using QoSs

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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.

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Data Availability

The datasets analysed during the current study are available in the QWS data repository, https://qwsdata.github.io/#.

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Acknowledgements

The authors are very grateful to the editor and reviewers to provide the valuable insight and remarks, which aided in the advancement of the manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India

    Lalit Purohit

  2. ABV-IIITM Gwalior, Gwalior, 474015, India

    Santosh S. Rathore

  3. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, 247667, India

    Sandeep Kumar

Authors
  1. Lalit Purohit
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  2. Santosh S. Rathore
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  3. Sandeep Kumar
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Correspondence to Sandeep Kumar.

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Appendix: A

Appendix: A

This section describes different performance measures that have been used to evaluate the quality of the service clusters (Given in Table 13). Furthermore, this section provides details of the used clustering techniques (Given in Table 14).

Table 13 Description of used performance measures [7]
Full size table
Table 14 Clustering techniques for web service clustering
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We have used R programming environment packages to implement PCA and the used clustering techniques. prcomp() package is used to implement principal component analysis and clvalid() package is used to implement and validate clustering techniques. The used parameter details of these techniques are given in Table 15).

Table 15 Parameter details of the PCA and the used clustering techniques
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Purohit, L., Rathore, S.S. & Kumar, S. Feature selection and clustering based web service selection using QoSs. Appl Intell 53, 13352–13377 (2023). https://doi.org/10.1007/s10489-022-04042-w

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