A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams

Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources t...

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Detalles Bibliográficos
Autores: García-Vico, Ángel M., Carmona, Cristóbal J., González, Pedro, del Jesus, María José
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4304
Acceso en línea:https://doi.org/10.1016/j.eswa.2021.115419
https://hdl.handle.net/10953/4304
Access Level:acceso abierto
Palabra clave:Big dataData stream mining
Evolutionary algorithms
Fuzzy logic
Emerging pattern mining
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spelling A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streamsGarcía-Vico, Ángel M.Carmona, Cristóbal J.González, Pedrodel Jesus, María JoséBig dataData stream miningEvolutionary algorithmsFuzzy logicEmerging pattern miningToday, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming whose objective is to monitor the state of the system with respect to a variable of interest. For this purpose, the proposed algorithm is a cellular-based multi-objective evolutionary fuzzy system that uses an informed strategy for efficient data processing and a re-initialisation and filtering mechanism to eliminate redundant and low-reliable patterns. The experimental study carried out demonstrates an interpretability improvement of 25% in the extraction of high-interest knowledge by the proposed algorithm, which would make it easier for experts to analyse the problem. Finally, the proposed algorithm is up to five times faster than another proposal on the processing of the same amount of data. In this experimental study, up to 750,000 instances have been processed in approximately four seconds.Springer202520252021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.eswa.2021.115419https://hdl.handle.net/10953/4304reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésExpert Systems with Applications 2021; Volume 183; 115419info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/43042026-06-24T12:41:07Z
dc.title.none.fl_str_mv A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
title A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
spellingShingle A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
García-Vico, Ángel M.
Big dataData stream mining
Evolutionary algorithms
Fuzzy logic
Emerging pattern mining
title_short A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
title_full A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
title_fullStr A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
title_full_unstemmed A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
title_sort A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
dc.creator.none.fl_str_mv García-Vico, Ángel M.
Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author García-Vico, Ángel M.
author_facet García-Vico, Ángel M.
Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author_role author
author2 Carmona, Cristóbal J.
González, Pedro
del Jesus, María José
author2_role author
author
author
dc.subject.none.fl_str_mv Big dataData stream mining
Evolutionary algorithms
Fuzzy logic
Emerging pattern mining
topic Big dataData stream mining
Evolutionary algorithms
Fuzzy logic
Emerging pattern mining
description Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming whose objective is to monitor the state of the system with respect to a variable of interest. For this purpose, the proposed algorithm is a cellular-based multi-objective evolutionary fuzzy system that uses an informed strategy for efficient data processing and a re-initialisation and filtering mechanism to eliminate redundant and low-reliable patterns. The experimental study carried out demonstrates an interpretability improvement of 25% in the extraction of high-interest knowledge by the proposed algorithm, which would make it easier for experts to analyse the problem. Finally, the proposed algorithm is up to five times faster than another proposal on the processing of the same amount of data. In this experimental study, up to 750,000 instances have been processed in approximately four seconds.
publishDate 2021
dc.date.none.fl_str_mv 2021
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.eswa.2021.115419
https://hdl.handle.net/10953/4304
url https://doi.org/10.1016/j.eswa.2021.115419
https://hdl.handle.net/10953/4304
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications 2021; Volume 183; 115419
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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