Imputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction

Authors

  • Gaurav Raj PhD. Scholar, IKG Punjab Technical University, Kapurthala
  • Manish Mahajan CGC College of Engineering, Landran, Mohali
  • Dheerendra Singh Chandigarh College of Engineering and Technology, Chandigarh
  • Anjali Singh University of Sydney, Australia

DOI:

https://doi.org/10.37591/rtpl.v6i2.2278

Abstract

Abstract

The web services at present have countless options for similar tasks. This wide range in web services induce challenge to choose the best service among all available. QoS prediction is a key of the selection but it is very time-consuming affair. Any prediction strategy relies on accuracy and completeness of available data, especially in case of QOS Prediction. Feedback, throughput and response time are the major attribute that should not be missed and incorrect. So, it's important to identify the missing value in the web service datasets. Therefore, a study of three missing value prediction approaches was undertaken to investigate their performance for missing values in datasets for web service. Benchmarked WS Dream dataset include response time and throughput matrices of web services is selected to analyze the performance of selected approaches. An extensive experiment is performed, and results are collected, which conclude the superiority of MICE approach over other approaches.

 

Keywords: WS Dream, MICE, kNN, missForest, MAE, MSE, RMSE, QoS

Cite this Article

Gaurav Raj, Manish Mahajan, Dheerendra Singh, Anjali Singh. Imputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction. Recent Trends in Programming languages. 2019; 6(2): 9–23p.


 

Downloads

Published

2019-09-03

Issue

Vol. 6 No. 2 (2019)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.