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Autodispatcher: a fully automated failure analyzer

Bibliographic Details
Main Author: Vieira, Daniela Domingos
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10451/64342
Summary: Trabalho de projeto de mestrado, Ciência de Dados , 2023, Universidade de Lisboa, Faculdade de Ciências
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network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Autodispatcher: a fully automated failure analyzerMachine LearningRedes NeuronaisÁrvores de DecisãoPythonTensorFlowTrabalhos de projeto de mestrado - 2024Departamento de InformáticaTrabalho de projeto de mestrado, Ciência de Dados , 2023, Universidade de Lisboa, Faculdade de CiênciasIn order to minimize the impact of potential failures of assets within its telecommunications infrastructure in Portugal, NOS is interested in improving the efficiency of current decision processes which are led by human operators. Currently, an expert team manually selects response teams to deal with occurred failures, based on a pre-compiled set of policies to classify alarms. However, NOS aims to leverage ML-based techniques to help the expert team select the most appropriate response team to deal with an occurred technical issue. Towards that goal, one of the main challenges lies in the highly dynamic nature of the alarms issued by the company’s large and diverse infrastructure. In this project, we explore the design of a fully automated failure analyzer based on neural networks, called Autodispatcher, to tackle this challenge. The Autodispatcher framework will function as a decision tree, capable of efficiently analyzing and processing real-time alarms emitted by the infrastructure monitoring tools. It is designed to have four stages, including two stages which are the main focus of this work: a string frequency analysis method and a neural network approach for word analysis. To ensure the efficiency and effectiveness of the Autodispatcher, regular model updates and retraining are conducted. This allows the system to adapt to changes in the infrastructure and incorporate new data patterns that may arise over time. Additionally, human operators play a considerable role in supervising and validating the Autodispatcher’s decisions, because they have the ability to review and adjust the system’s recommendations. The main goal of the Autodispatcher is to minimize response time and optimize resource allocation. By automating the process of selecting response teams, NOS can significantly improve the efficiency of its operations. This reduces the impact of failures on the infrastructure and enhances the overall customer experience by ensuring timely resolutions to technical issues.Garcia, Nuno Ricardo da CruzGuimarães, Luís Manuel RodriguesRepositório da Universidade de LisboaVieira, Daniela Domingos2024-04-16T16:46:11Z202420232024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/64342enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T15:14:41Zoai:repositorio.ulisboa.pt:10451/64342Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T03:37:47.135856Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Autodispatcher: a fully automated failure analyzer
title Autodispatcher: a fully automated failure analyzer
spellingShingle Autodispatcher: a fully automated failure analyzer
Vieira, Daniela Domingos
Machine Learning
Redes Neuronais
Árvores de Decisão
Python
TensorFlow
Trabalhos de projeto de mestrado - 2024
Departamento de Informática
title_short Autodispatcher: a fully automated failure analyzer
title_full Autodispatcher: a fully automated failure analyzer
title_fullStr Autodispatcher: a fully automated failure analyzer
title_full_unstemmed Autodispatcher: a fully automated failure analyzer
title_sort Autodispatcher: a fully automated failure analyzer
author Vieira, Daniela Domingos
author_facet Vieira, Daniela Domingos
author_role author
dc.contributor.none.fl_str_mv Garcia, Nuno Ricardo da Cruz
Guimarães, Luís Manuel Rodrigues
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Vieira, Daniela Domingos
dc.subject.por.fl_str_mv Machine Learning
Redes Neuronais
Árvores de Decisão
Python
TensorFlow
Trabalhos de projeto de mestrado - 2024
Departamento de Informática
topic Machine Learning
Redes Neuronais
Árvores de Decisão
Python
TensorFlow
Trabalhos de projeto de mestrado - 2024
Departamento de Informática
description Trabalho de projeto de mestrado, Ciência de Dados , 2023, Universidade de Lisboa, Faculdade de Ciências
publishDate 2023
dc.date.none.fl_str_mv 2023
2024-04-16T16:46:11Z
2024
2024-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/64342
url http://hdl.handle.net/10451/64342
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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