An Efficient Approach for Selecting QoS-Based Web Service Machine Learning Models Using Topsis

Miguel Angel Quiroz Martinez, Josue Leonardo Moncayo Redin, Erick David Alvarado Castillo, Luis Andy Briones Peñafiel

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 27th International Conference on Systems Engineering, ICSEng 2020
EditoresHenry Selvaraj, Grzegorz Chmaj, Dawid Zydek
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas172-182
Número de páginas11
ISBN (versión impresa)9783030657956
DOI
EstadoPublicada - 2021
Evento27th International Conference on Systems Engineering, ICSEng 2020 - Las Vegas, Estados Unidos
Duración: 14 dic. 202016 dic. 2020

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen182
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia27th International Conference on Systems Engineering, ICSEng 2020
País/TerritorioEstados Unidos
CiudadLas Vegas
Período14/12/2016/12/20

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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