Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis
Ano de defesa: | 2019 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Pontif?cia Universidade Cat?lica do Rio Grande do Sul
|
Programa de Pós-Graduação: |
Programa de P?s-Gradua??o em Ci?ncia da Computa??o
|
Departamento: |
Escola Polit?cnica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://tede2.pucrs.br/tede2/handle/tede/8781 |
Resumo: | It is possible to observe a significant growth in the use of mobile devices as well as the use of applications on such devices over the last years. In addition, the technological innovation and fierce dispute to conquer the market make mobile device manufacturers companies increase their attention to the interests of their clients. These clients perform daily many activities through the use of applications, which generates, in real time, a large number of events. Therefore, it is important for aforementioned companies to understand how their customers use applications on their devices. In this sense, automatic mechanisms, capable of assisting in the identification and monitoring of profiles and behavior of such clients, can contribute to the decision making of the stackholders. Based on this, this study proposes a framework for the identification and monitoring of the profiles and behaviors of app usage on mobile devices. To achieve this goal, Data Mining techniques such as Transformation and Discretization, Machine Learning tasks such as Association Rules and Clustering, and Novelty Detection techniques such as Concept Drift and Concept Evolution, are used to explore the app usage, identify app usage patterns, pinpoint profiles, and monitor customer behaviors over time. In order to make a comparative analysis, we have evaluated the approaches adopted by the literature, considering a real app usage data stream. Results of the experimental analysis show that the proposed framework presents better results to the addressed scenario pointing to profiles and behaviors that evolve according to the data stream. |
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Ruiz, Duncan Dubugras AlcobaMachado, Nielsen Luiz Rechia2019-07-03T14:53:02Z2019-03-27http://tede2.pucrs.br/tede2/handle/tede/8781It is possible to observe a significant growth in the use of mobile devices as well as the use of applications on such devices over the last years. In addition, the technological innovation and fierce dispute to conquer the market make mobile device manufacturers companies increase their attention to the interests of their clients. These clients perform daily many activities through the use of applications, which generates, in real time, a large number of events. Therefore, it is important for aforementioned companies to understand how their customers use applications on their devices. In this sense, automatic mechanisms, capable of assisting in the identification and monitoring of profiles and behavior of such clients, can contribute to the decision making of the stackholders. Based on this, this study proposes a framework for the identification and monitoring of the profiles and behaviors of app usage on mobile devices. To achieve this goal, Data Mining techniques such as Transformation and Discretization, Machine Learning tasks such as Association Rules and Clustering, and Novelty Detection techniques such as Concept Drift and Concept Evolution, are used to explore the app usage, identify app usage patterns, pinpoint profiles, and monitor customer behaviors over time. In order to make a comparative analysis, we have evaluated the approaches adopted by the literature, considering a real app usage data stream. Results of the experimental analysis show that the proposed framework presents better results to the addressed scenario pointing to profiles and behaviors that evolve according to the data stream.? poss?vel observar um crescimento significativo no uso de dispositivos m?veis, bem como na utiliza??o de aplicativos nestes dispositivos ao longo dos ?ltimo anos. Al?m disso, a inova??o tecnol?gica e a disputa acirrada na conquista do mercado faz com que empresas fabricantes de tais dispositivos aumentem suas aten??es para interesses de seus clientes. Estes clientes realizam diariamente muitas atividades por meio do uso de aplicativos, o que gera, em tempo real, uma grande quantidade de eventos. Diante disso, ? importante para estas empresas entender como seus clientes utilizam aplicativos em seus dispositivos. Neste sentido, mecanismos autom?ticos capazes de ajudar na identifica??o e no monitoramento de perfis e comportamento de tais clientes, podem contribuir na tomada de decis?es das partes interessadas. Assim, esta pesquisa prop?e um framework para identifica??o e monitoramento de perfis e comportamentos de uso de aplicativos em dispositivos m?veis. Para alcan?ar este objetivo, t?cnicas de Minera??o de dados como, Transforma??o e Discretiza??o, tarefas de Aprendizado de M?quina como, Regras de Associa??o e Agrupamento, e t?cnicas de Detec??o de Novidade como, Mudan?a e Evolu??o de Conceito s?o utilizadas. Com o objetivo de fazer uma an?lise comparativa, foram avaliados abordagens relatadas na literatura, considerando para tanto, um fluxo cont?nuio de dados de uso de aplicativos real. Resultados da an?lise experimental mostram que o framework proposto apresenta melhores resultados ao cen?rio abordado apontando perfis e comportamentos que evoluem conforme o fluxo cont?nuo de dados.Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2019-07-03T11:34:50Z No. of bitstreams: 1 NIELSEN LUIZ RECHIA MACHADO_TES.pdf: 9617308 bytes, checksum: b4cfa6650b3131d4457ab0cbb7760f8f (MD5)Approved for entry into archive by Sarajane Pan (sarajane.pan@pucrs.br) on 2019-07-03T13:59:15Z (GMT) No. of bitstreams: 1 NIELSEN LUIZ RECHIA MACHADO_TES.pdf: 9617308 bytes, checksum: b4cfa6650b3131d4457ab0cbb7760f8f (MD5)Made available in DSpace on 2019-07-03T14:53:02Z (GMT). No. of bitstreams: 1 NIELSEN LUIZ RECHIA MACHADO_TES.pdf: 9617308 bytes, checksum: b4cfa6650b3131d4457ab0cbb7760f8f (MD5) Previous issue date: 2019-03-27application/pdfhttp://tede2.pucrs.br:80/tede2/retrieve/175841/NIELSEN%20LUIZ%20RECHIA%20MACHADO_TES.pdf.jpgporPontif?cia Universidade Cat?lica do Rio Grande do SulPrograma de P?s-Gradua??o em Ci?ncia da Computa??oPUCRSBrasilEscola Polit?cnicaIdentifica??o de PerfisMonitoramento de PerfisMonitoramento de ComportamentosAprendizado de M?quinaAplicativos M?veisProfile IdentificationProfile MonitoringBehavior MonitoringMachine LearningMobile AppsCIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAOUm framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisTrabalho n?o apresenta restri??o para publica??o-4570527706994352458500500-862078257083325301info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da PUC_RSinstname:Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)instacron:PUC_RSTHUMBNAILNIELSEN LUIZ RECHIA MACHADO_TES.pdf.jpgNIELSEN LUIZ RECHIA MACHADO_TES.pdf.jpgimage/jpeg5844http://tede2.pucrs.br/tede2/bitstream/tede/8781/4/NIELSEN+LUIZ+RECHIA+MACHADO_TES.pdf.jpgeab342e9658018976ac800cc9868ee23MD54TEXTNIELSEN LUIZ RECHIA MACHADO_TES.pdf.txtNIELSEN LUIZ RECHIA MACHADO_TES.pdf.txttext/plain473763http://tede2.pucrs.br/tede2/bitstream/tede/8781/3/NIELSEN+LUIZ+RECHIA+MACHADO_TES.pdf.txta0e6c829825c10b980eece25e1ef3e4fMD53ORIGINALNIELSEN LUIZ RECHIA MACHADO_TES.pdfNIELSEN LUIZ RECHIA MACHADO_TES.pdfapplication/pdf9617308http://tede2.pucrs.br/tede2/bitstream/tede/8781/2/NIELSEN+LUIZ+RECHIA+MACHADO_TES.pdfb4cfa6650b3131d4457ab0cbb7760f8fMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8590http://tede2.pucrs.br/tede2/bitstream/tede/8781/1/license.txt220e11f2d3ba5354f917c7035aadef24MD51tede/87812019-07-03 20:00:33.488oai:tede2.pucrs.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede2.pucrs.br/tede2/PRIhttps://tede2.pucrs.br/oai/requestbiblioteca.central@pucrs.br||opendoar:2019-07-03T23:00:33Biblioteca Digital de Teses e Dissertações da PUC_RS - Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)false |
dc.title.por.fl_str_mv |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
title |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
spellingShingle |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis Machado, Nielsen Luiz Rechia Identifica??o de Perfis Monitoramento de Perfis Monitoramento de Comportamentos Aprendizado de M?quina Aplicativos M?veis Profile Identification Profile Monitoring Behavior Monitoring Machine Learning Mobile Apps CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
title_short |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
title_full |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
title_fullStr |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
title_full_unstemmed |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
title_sort |
Um framework para identifica??o e monitoramento de perfis e comportamentos de consumidores baseado no uso de aplicativos em dispositivos m?veis |
author |
Machado, Nielsen Luiz Rechia |
author_facet |
Machado, Nielsen Luiz Rechia |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ruiz, Duncan Dubugras Alcoba |
dc.contributor.author.fl_str_mv |
Machado, Nielsen Luiz Rechia |
contributor_str_mv |
Ruiz, Duncan Dubugras Alcoba |
dc.subject.por.fl_str_mv |
Identifica??o de Perfis Monitoramento de Perfis Monitoramento de Comportamentos Aprendizado de M?quina Aplicativos M?veis |
topic |
Identifica??o de Perfis Monitoramento de Perfis Monitoramento de Comportamentos Aprendizado de M?quina Aplicativos M?veis Profile Identification Profile Monitoring Behavior Monitoring Machine Learning Mobile Apps CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Profile Identification Profile Monitoring Behavior Monitoring Machine Learning Mobile Apps |
dc.subject.cnpq.fl_str_mv |
CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
description |
It is possible to observe a significant growth in the use of mobile devices as well as the use of applications on such devices over the last years. In addition, the technological innovation and fierce dispute to conquer the market make mobile device manufacturers companies increase their attention to the interests of their clients. These clients perform daily many activities through the use of applications, which generates, in real time, a large number of events. Therefore, it is important for aforementioned companies to understand how their customers use applications on their devices. In this sense, automatic mechanisms, capable of assisting in the identification and monitoring of profiles and behavior of such clients, can contribute to the decision making of the stackholders. Based on this, this study proposes a framework for the identification and monitoring of the profiles and behaviors of app usage on mobile devices. To achieve this goal, Data Mining techniques such as Transformation and Discretization, Machine Learning tasks such as Association Rules and Clustering, and Novelty Detection techniques such as Concept Drift and Concept Evolution, are used to explore the app usage, identify app usage patterns, pinpoint profiles, and monitor customer behaviors over time. In order to make a comparative analysis, we have evaluated the approaches adopted by the literature, considering a real app usage data stream. Results of the experimental analysis show that the proposed framework presents better results to the addressed scenario pointing to profiles and behaviors that evolve according to the data stream. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-07-03T14:53:02Z |
dc.date.issued.fl_str_mv |
2019-03-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
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http://tede2.pucrs.br/tede2/handle/tede/8781 |
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http://tede2.pucrs.br/tede2/handle/tede/8781 |
dc.language.iso.fl_str_mv |
por |
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por |
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500 500 |
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info:eu-repo/semantics/openAccess |
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Pontif?cia Universidade Cat?lica do Rio Grande do Sul |
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Programa de P?s-Gradua??o em Ci?ncia da Computa??o |
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PUCRS |
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Brasil |
dc.publisher.department.fl_str_mv |
Escola Polit?cnica |
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Pontif?cia Universidade Cat?lica do Rio Grande do Sul |
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