Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata

Detalhes bibliográficos
Autor(a) principal: Corbisier, Leonardo Leal
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/97875
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Adding Kalman filter into ESI: an anomaly detection approach for middleware metadataBig‐DataAnomaly detectionKalman filterBorder InnovationSplunkInternship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis report treats about the implementation of the Kalman filter algorithm into a product of Border Innovation ‐ ESI – which had the goal to complement the range of algorithms available in the tool. Most specifically it treats about using the referred algorithm to point out anomalous events on a very particular Big‐data scenario, which is the metadata about the data flowing in streaming through a middleware software. The focus of this report thus relies on providing reasoning about the problem which the algorithm has to deal with, and the data treatment adopted to implement the algorithm into the referred tool. It is relevant to mention that the practical implementation was done using Splunk, and therefore the technological aspects and the language of the tool were important factors that guided the algorithm implementation. The project took place during the author’s work at the company and the data used to guide the project reflects the reality the tool is built to deal with, and it is fully anonymized to preserve the company interest.Castelli, MauroRUNCorbisier, Leonardo Leal2022-04-30T00:30:54Z2020-04-302020-04-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/97875TID:202484165enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:44:45Zoai:run.unl.pt:10362/97875Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:49.125454Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
title Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
spellingShingle Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
Corbisier, Leonardo Leal
Big‐Data
Anomaly detection
Kalman filter
Border Innovation
Splunk
title_short Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
title_full Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
title_fullStr Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
title_full_unstemmed Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
title_sort Adding Kalman filter into ESI: an anomaly detection approach for middleware metadata
author Corbisier, Leonardo Leal
author_facet Corbisier, Leonardo Leal
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Corbisier, Leonardo Leal
dc.subject.por.fl_str_mv Big‐Data
Anomaly detection
Kalman filter
Border Innovation
Splunk
topic Big‐Data
Anomaly detection
Kalman filter
Border Innovation
Splunk
description Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2020
dc.date.none.fl_str_mv 2020-04-30
2020-04-30T00:00:00Z
2022-04-30T00:30:54Z
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/10362/97875
TID:202484165
url http://hdl.handle.net/10362/97875
identifier_str_mv TID:202484165
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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