dc.contributor | Fonseca, Mauro Sergio Pereira | |
dc.contributor | http://lattes.cnpq.br/6534637358360971 | |
dc.contributor | Vendramin, Ana Cristina Barreiras Kochem | |
dc.contributor | http://lattes.cnpq.br/3005557336605080 | |
dc.contributor | Fonseca, Mauro Sergio Pereira | |
dc.contributor | Pedroso, Carlos Marcelo | |
dc.contributor | Nacamura Júnior, Luiz | |
dc.contributor | Tacla, Cesar Augusto | |
dc.creator | Felix, Kleber Gonçalves | |
dc.date.accessioned | 2017-12-27T01:00:46Z | |
dc.date.accessioned | 2022-12-06T14:26:53Z | |
dc.date.available | 2017-12-27T01:00:46Z | |
dc.date.available | 2022-12-06T14:26:53Z | |
dc.date.created | 2017-12-27T01:00:46Z | |
dc.date.issued | 2017-08-29 | |
dc.identifier | FELIX, Kleber Gonçalves. Classificação automática de falhas em arquitetura orientada a serviços. 2017. 90 f. Dissertação (Mestrado em Computação Aplicada) - Universidade Tecnológica Federal do Paraná, Curitiba, 2017. | |
dc.identifier | http://repositorio.utfpr.edu.br/jspui/handle/1/2822 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5249222 | |
dc.description.abstract | A distributed architecture is composed of many systems that exchange messages between each other. Faults in the integration of these systems may occur and they required a detailed investigation of support professionals to identifying the root cause of the problem. The manual process to identify causes of failure is difficult and time-consuming. Significant efficiency gains can be achieved by automating the faults classification process. This work presents a method to support the automated fault diagnostic process, automatically classifying faults generated in a Service Oriented Architecture (SOA). This method denominated SOAFaultControl, may be executed in a distributed architecture that adote SOA and an Enterprise Service Bus (ESB). Using machine learning techniques, was possible build a model to classify fault messages captured in a SOA environment, in pre-established classes. To achieve the objectives of this work it was necessary to test the following machine learning algorithms: Support Vector Machine, Naive Bayes, and AdaBoost. Results show that Support Vector Machine algorithm achieved better performance in the following metrics: precision, accuracy, recall, and F1. | |
dc.publisher | Universidade Tecnológica Federal do Paraná | |
dc.publisher | Curitiba | |
dc.publisher | Brasil | |
dc.publisher | Programa de Pós-Graduação em Computação Aplicada | |
dc.publisher | UTFPR | |
dc.rights | openAccess | |
dc.subject | Sistemas operacionais distribuídos (Computadores) | |
dc.subject | Localização de falhas (Engenharia) | |
dc.subject | Controle automático | |
dc.subject | Aprendizado do computador | |
dc.subject | Arquitetura orientada a serviços (Computador) | |
dc.subject | Algorítmos | |
dc.subject | Métodos de simulação | |
dc.subject | Computação | |
dc.subject | Distributed operating systems (Computers) | |
dc.subject | Fault location (Engineering) | |
dc.subject | Automatic control | |
dc.subject | Machine learning | |
dc.subject | Service-oriented architecture (Computer science) | |
dc.subject | Algorithms | |
dc.subject | Simulation methods | |
dc.subject | Computer science | |
dc.title | Classificação automática de falhas em arquitetura orientada a serviços | |
dc.type | masterThesis | |