Open Access

ARTICLE

Prediction of Web Services Reliability Based on Decision Tree Classification Method

Zhichun Jia^{1, 2}, Qiuyang Han¹, Yanyan Li¹, Yuqiang Yang¹, Xing Xing^{1, 2, *}

1 College of Information Science and Technology, Bohai University, Jinzhou, 121013, China.
2 School of Engineering, The University of New Mexico, Albuquerque, NM 87131, USA.

* Corresponding Author: Xing Xing. Email: .

Computers, Materials & Continua 2020, 63(3), 1221-1235. https://doi.org/10.32604/cmc.2020.09722

Received 16 January 2020; Accepted 24 February 2020; Issue published 30 April 2020

Abstract

With the development of the service-oriented computing (SOC), web service has an important and popular solution for the design of the application system to various enterprises. Nowadays, the numerous web services are provided by the service providers on the network, it becomes difficult for users to select the best reliable one from a large number of services with the same function. So it is necessary to design feasible selection strategies to provide users with the reliable services. Most existing methods attempt to select services according to accurate predictions for the quality of service (QoS) values. However, because the network and user needs are dynamic, it is almost impossible to accurately predict the QoS values. Furthermore, accurate prediction is generally timeconsuming. This paper proposes a service decision tree based post-pruning prediction approach. This paper first defines the five reliability levels for measuring the reliability of services. By analyzing the quality data of service from the network, the proposed method can generate the training set and convert them into the service decision tree model. Using the generated model and the given predicted services, the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service. Moreover, this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting. Experimental results show that the proposed method is effective in predicting the service reliability.

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Citations