Classification of Web Services for Efficient Performability

doi:10.23940/ijpe.23.10.p3.654662

Abstract

Abstract: The use of the Internet for business is increasing day by day, as is the use of web services. Web services are the main components of business over the Internet. Several service providers are competing for their web services to be used. The enormous growth in the number of online services has created difficulty with web service classification. Manually classifying and choosing web services for an application is a highly challenging job that often leads to ineffective, error-prone outcomes. To use online services effectively, automatic and precise categorization is necessary. The primary objective of this research is to classify online services using various machine learning models, compare them, and determine which model is the most effective. To better comprehend the application of these classifiers on various web services, the outcomes of several machine learning classifiers are compared based on various characteristics. The end results are realized in a table suggest that in terms of Accuracy, F1 Score, and MCC metrics, SVM and NN classifiers are shown to be equally best. However, in terms of Execution time, NB is best with 0.036 seconds and NN is worst with 3.41 seconds.

Key words: web-service, machine learning, classification, UDDI, WSDL, SOAP

Jitender Tanwar, Sanjay Kumar Sharma, Mandeep Mittal, and Ashok Kumar Yadav. Classification of Web Services for Efficient Performability [J]. Int J Performability Eng, 2023, 19(10): 654-662.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Mustafa, A.S. and Kumaraswamy, Y.S.Data Mining Algorithms for Web-Services Classification. In2014 International Conference on Contemporary Computing and Informatics (IC3I), IEEE, pp. 951-956, 2014.
[2] Tanwar J., Sharma S.K., andMittal M.Secure Framework for Web Services Communication. In2018 International Conference on Automation and Computational Engineering (ICACE), IEEE, pp. 187-190, 2018.
[3] Tanwar J., Sharma S.K., andMittal M.Faster and Secured Web Services Communication using Modified IDEA and Custom-Level Security. In Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2020, Springer Singapore, pp. 596-603, 2021.
[4] Sharma S., Lather J.S., andDave M.Semantic Approach for Web Service Classification using Machine Learning and Measures of Semantic Relatedness. Service Oriented Computing and Applications, vol. 10, pp. 221-231, 2016.
[5] Zhu W., Yin B., Gong S., andCai K.Y.An Approach to Web Services Selection for Multiple Users. IEEE Access, vol. 5, pp. 15093-15104, 2017.
[6] Tanwar J., Sharma S.K., andMittal M.Designing Obstacle’S Map of an Unknown Place using Autonomous Drone Navigation and Web Services. International Journal of Pervasive Computing and Communications, vol. 19, no. 1, pp. 154-169, 2023.
[7] Chippa M., Priyadarshini A., andMohanty R.Application of Machine Learning Techniques to Classify Web Services. In2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), IEEE, pp. 1-7, 2019.
[8] Saravanan, R. and Sujatha, P.A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification. In2018 Second international conference on intelligent computing and control systems (ICICCS), IEEE, pp. 945-949, 2018.
[9] Yang, J. and Zhou, X.Semi-Automatic Web Service Classification using Machine Learning. International Journal of u-and e-Service, Science and Technology, vol. 8, no. 4, pp. 339-348, 2015.
[10] Pradhan T., Walia V., Kapoor R., andSaran S.Optimizing Land Use Classification using Decision Tree Approaches. In2014 International Conference on Data Mining and Intelligent Computing (ICDMIC), IEEE, pp. 1-5, 2014.
[11] Schmidhuber J.Deep Learning in Neural Networks: An Overview. Neural networks, vol. 61, pp. 85-117, 2015.
[12] Yoon, C.Y. and Kang, S.G.A Study on Machine Learning Web Service. In2017 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, pp. 760-765, 2017.
[13] Raj T., Michael T.F., Ravichandran K., andRajesh K.Domain Specific Web Service Composition by Parameter Classification using NaïVe Bayes Algorithm. World Applied Sciences Journal, vol. 29, pp. 99-105, 2014.
[14] Youn, E. and Jeong, M.K.Class Dependent Feature Scaling Method using Naive Bayes Classifier for Text Datamining. Pattern Recognition Letters, vol. 30, no. 5, pp. 477-485, 2009.
[15] Al-Baity, H.H. and AlShowiman, N.I. Towards Effective Service Discovery using Feature Selection and Supervised Learning Algorithms. International Journal of Advanced Computer Science and Applications, vol. 10, no. 5, 2019.
[16] Yang Y., Ke W., Wang W., andZhao Y.Deep Learning for Web Services Classification. In2019 IEEE International conference on Web services (ICWS), IEEE, pp. 440-442, 2019.