dc.contributorFonseca, Mauro Sergio Pereira
dc.contributorhttp://lattes.cnpq.br/6534637358360971
dc.contributorVendramin, Ana Cristina Barreiras Kochem
dc.contributorhttp://lattes.cnpq.br/3005557336605080
dc.contributorFonseca, Mauro Sergio Pereira
dc.contributorPedroso, Carlos Marcelo
dc.contributorNacamura Júnior, Luiz
dc.contributorTacla, Cesar Augusto
dc.creatorFelix, Kleber Gonçalves
dc.date.accessioned2017-12-27T01:00:46Z
dc.date.accessioned2022-12-06T14:26:53Z
dc.date.available2017-12-27T01:00:46Z
dc.date.available2022-12-06T14:26:53Z
dc.date.created2017-12-27T01:00:46Z
dc.date.issued2017-08-29
dc.identifierFELIX, 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.identifierhttp://repositorio.utfpr.edu.br/jspui/handle/1/2822
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5249222
dc.description.abstractA 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.publisherUniversidade Tecnológica Federal do Paraná
dc.publisherCuritiba
dc.publisherBrasil
dc.publisherPrograma de Pós-Graduação em Computação Aplicada
dc.publisherUTFPR
dc.rightsopenAccess
dc.subjectSistemas operacionais distribuídos (Computadores)
dc.subjectLocalização de falhas (Engenharia)
dc.subjectControle automático
dc.subjectAprendizado do computador
dc.subjectArquitetura orientada a serviços (Computador)
dc.subjectAlgorítmos
dc.subjectMétodos de simulação
dc.subjectComputação
dc.subjectDistributed operating systems (Computers)
dc.subjectFault location (Engineering)
dc.subjectAutomatic control
dc.subjectMachine learning
dc.subjectService-oriented architecture (Computer science)
dc.subjectAlgorithms
dc.subjectSimulation methods
dc.subjectComputer science
dc.titleClassificação automática de falhas em arquitetura orientada a serviços
dc.typemasterThesis


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