Abstract
Web-based applications are more popular and are increasing the demand to use of applications. Service quality and user satisfaction are the most significant for the designer. IT provides the best solutions for various applications such as B2B, B2C, e-commerce and other applications. The client demands high-quality service, regarding minimum response time, high availability and more security. The existing approaches and models may not improve the overall performance of web-based application due to not covering all functional, nonfunctional parameters. The proposed model-approach gives the best solution using rule-based classification of web services using QWS dataset, and predictions of quality parameters based on user specifications. The model is implemented in Java, the results of quality parameters will classify and predict the class labels Class-1(high quality), Class-2, Class-3 and Class-4 (low quality), and the system will give recommendations to improve the quality parameters. By using suggested guidelines and instructions to the software developer, that he will meet the client specifications and provides best quality values which will improve the overall performance (in specification parameters including functional and nonfunctional values). The result clearly suggests the improvement of quality parameters by classification and prediction. This paper can be extended to mixed attributes of quality parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Global Digital Report. https://wearesocial.com/blog/2018/01/global-digital-report-2018.
Weiss, M., B. Esfandiari, Y. Luo. 2007. Towards a Classification of Web Service Feature Interactions. Computer Networks 51: 359–381.
Zhang, Jing, Dan Pan. 2008. Web Service Classification. https://pdfs.semanticscholar.org.
Wang, Hongbing, Yanqi Shi, Xuan Zhouy, and Qianzhao Zhou. 2008. Web Service Classification using Support Vector Machine. In International Conference on Tools with Artificial Intelligence, 1–6. IEEE.
Mohanty, Ramakanta, V. Ravi, and M. R. Patra. 2010. Web-Services Classification Using Intelligent Techniques. Expert Systems with Applications 5484–5490.
Bunkar, K., U.K. Singh, B. Pandya, and R. Bunkar. 2012. Data Mining: Prediction for Performance Improvement of Graduate Students Using Classification. In 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN), Indore, 1–5.
Bennaceur, Amel, Valerie Issarny, Richard Johansson, Alessandro Moschitti, Romina Spalazzese, and Daniel Sykes. 2012. Machine Learning for Automatic Classification of Web Service Interface Descriptions. http://dit.unitn.it/moschitti/articles/2012/ISOLA2011.pdf.
Mohanty, Ramakanta, V. Ravi, M.R. Patra. 2012. Classification of Web Services Using Bayesian Network. Journal of Software Engineering and Applications 291–296.
Syed Mustafa, A., and Y.S. Kumaraswamy. 2014. Performance Evaluation of Web-Services Classification. Indian Journal of Science and Technology 7 (10), 1674–1681.
Yang, Jie, and Xinzhong Zhou. 2015. Semi-automatic Web Service Classification Using Machine Learning. International Journal of U and e-service, science and Technology 8 (4): 339–348.
Hamdi, Yahyaoui, Hala Own, and Zaki Malik. 2015. Modeling and Classification of Service Behaviors. Expert Systems with Applications 1–24.
Kvasnicovaa, Terezia, Iveta Kremenovaa, and Juraj Fabusa. 2016. From an Analysis of e-Services Definitions and Classifications to the Proposal of New e-Service Classification: 3rd Global Conference On Business, Economics, Management and Tourism, 192–196. Amsterdam: Elsevier.
Reyes-Ortiz, Jose A., Maricela Bravo, and Hugo Pablo. 2016. Web Services Ontology Population through Text Classification. In Proceedings of the Federated Conference on Computer Science and Information Systems, 491–495.
Kumar, Lov, Santanu Rath, and Ashish Sureka. 2017. Estimating Web Service Quality of Service Parameters Using Source Code Metrics and LSSVM. In 5th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2017), 66–73.
Xiong, Ruibin, Jian Wang, Neng Zhang, and Yutao Ma. 2018. Deep Hybrid Collaborative Filtering for Web Service Recommendation. Expert Systems with Applications 191–205.
Zhang, Neng, Jian Wang, Yutao Ma, Keqing He, Zheng Li, and Xiaoqing Liu. 2018. Web Service Discovery Based on Goal-Oriented Query Expansion. The Journal of Systems and Software 73–91.
Al-Helal, H., and R. Gamble. 2013. Introducing Replaceability into Web Service Composition. IEEE Transactions on Services Computing 1–30.
Lalit, Purohit, Sandeep Kumar. 2018. A Classification Based Web Service Selection Approach. IEEE Transactions on Service Computing 1–14.
QWS Data Set. http://www.uoguelph.ca/~qmahmoud/qws/.
Swami Das, M., A. Govardhan, and D. Vijaya Lakshmi. 2018. Web Services Classification Across Cloud-Based Applications. In Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 742, ed. K. Ray, T. Sharma, S. Rawat, R. Saini, and A. Bandyopadhyay, 245–260. Singapore: Springer.
Hamdi, Yahyaoui, Hala Own, and Zaki Malak. 2014. Modeling and Classification of Service Behavior. Expert Systems, 1–27.
Xiao, Jian, et al. 2016. An Approach of Sematic Web Service Classification Based on Naive Bays, 1–7. IEEE.
Kalyani, G., and M.V.P. Chandra Sekhara Rao. 2017. Particle Swarm Intelligence and Impact Factor-Based Privacy Preserving Association Rule Mining for Balancing Data Utility and Knowledge Privacy. Arabian Journal for Science and Engineering.
Ramakrishna Murty, M., J.V.R. Murthy, P.V.G.D. Prasad Reddy. 2011. Text Document Classification Based on a Least Square Support Vector Machines with Singular Value Decomposition. International Journal of Computer Application (IJCA) 27 (7).
Adeniyi, D.A., Z. Wei, and Y. Yongquan. 2016. Automated Web Usage Data Mining and Recommendation System Using K Nearest Neighbor (KNN) Classification Method. Applied Computing and Informatics 12: 90–108.
Bakraouy, Zineb, Amine Baina, Mostafa Bellafkih. 2018. Availability of Web Services Based on Autonomous Classification and Negotiation of SLAs, 1–6. IEEE.
Acknowledgements
Thanks to Dr. Eyhab Al-Masri for providing QWS dataset 2507 records. Thanks to Dr. R. K. Mohanty, Professor, KMIT, Hyderabad for his support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Swami Das, M., Govardhan, A., Vijaya Lakshmi, D. (2020). Web Service Classification and Prediction Using Rule-Based Approach with Recommendations for Quality Improvements. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)