Classification of web services using data mining algorithms and improved learning model

Abstract

As per the global digital report, 52.9% of the world population is using the internet, and 42% of the world population is actively using e-commerce, banking, and other online applications. Web services are software components accessed using networked communications and provide services to end users. Software developers provide a high quality of web service. To meet the demands of user requirements, it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset and found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conducted experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are compared with various techniques random forest, artificial neural network, J48 decision tree, extreme gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of 98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes.

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

“…Service applicants can search for the services they need on UDDI using only the keywords provided by the service providers. Currently, UDDI requires service providers to register their services in relevant categories, and requesters must search for the appropriate category and related services [26]. Web services can be classified manually or automatically, but manual classification methods are ineffective due to their large size and functional diversity.…”

Genetic Programming Approach for Classification Problem using GPU

“…Genetic programming algorithms have been applied to the resolution of numerous data mining problems. Data mining [2]- [4] is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. In today's information society, where the amount of data stored is multiplied almost exponentially every day, data mining is a fundamental tool to analyse and exploit it effectively.…”