Abstract
With the advancement of Service Oriented Architecture (SOA), web services have gained great popularity playing a vital role in performing daily transactions and information exchange based on the interaction of different applications within or outside through communication protocols, allowing to support the business requirements and data consolidation of any company. With the increase in the number of web services with the same functionalities, the problem that arises is that not all of them are efficient, which makes it difficult to make a decision to select the best ones that meet all the user’s requirements. The problem can be solved by considering the quality of web services to distinguish web services with similar functionality. The objective of this paper proposes several automatic learning models to classify web services in categories according to QoS attributes using a refined data set, then select the best model based on performance criteria through the TOPSIS method. The deductive method and exploratory research technique were applied to study the QWS dataset.
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Acknowledgments
This work has been supported by the GIIAR research group and the Salesian Polytechnic University of Guayaquil.
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Martinez, M.A.Q., Redin, J.L.M., Castillo, E.D.A., Peñafiel, L.A.B. (2021). An Efficient Approach for Selecting QoS-Based Web Service Machine Learning Models Using Topsis. In: Selvaraj, H., Chmaj, G., Zydek, D. (eds) Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020. ICSEng 2020. Lecture Notes in Networks and Systems, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-65796-3_16
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