Data stream mining in ubiquitous environments: state-of-the-art and current directions
Main Author: | |
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Publication Date: | 2014 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | http://repositorio.inesctec.pt/handle/123456789/5369 http://dx.doi.org/10.1002/widm.1115 |
Summary: | In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the . |
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Data stream mining in ubiquitous environments: state-of-the-art and current directionsIn this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .2018-01-03T10:39:17Z2014-01-01T00:00:00Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5369http://dx.doi.org/10.1002/widm.1115engGaber,MMJoão GamaKrishnaswamy,SGomes,JBStahl,Finfo: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:RCAAP2023-05-15T10:20:30Zoai:repositorio.inesctec.pt:123456789/5369Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:14.995549Repositório 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 |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
title |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
spellingShingle |
Data stream mining in ubiquitous environments: state-of-the-art and current directions Gaber,MM |
title_short |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
title_full |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
title_fullStr |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
title_full_unstemmed |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
title_sort |
Data stream mining in ubiquitous environments: state-of-the-art and current directions |
author |
Gaber,MM |
author_facet |
Gaber,MM João Gama Krishnaswamy,S Gomes,JB Stahl,F |
author_role |
author |
author2 |
João Gama Krishnaswamy,S Gomes,JB Stahl,F |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Gaber,MM João Gama Krishnaswamy,S Gomes,JB Stahl,F |
description |
In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the . |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01T00:00:00Z 2014 2018-01-03T10:39:17Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/5369 http://dx.doi.org/10.1002/widm.1115 |
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http://repositorio.inesctec.pt/handle/123456789/5369 http://dx.doi.org/10.1002/widm.1115 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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