Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ISCC53001.2021.9631495 http://hdl.handle.net/11449/230253 |
Resumo: | Mining association rules is a process which consists in extracting knowledge from datasets. This is a widely used technique to analyze customer purchasing patterns, and its process is segmented in two main phases: mining frequent sets and formulating association rules. Several approaches were developed for the first phase of the mining process whose main objective was to reduce execution time. However, as all available datasets are very large (Big Data), there is a limitation regarding its application in these new sets due to excessive memory usage. We propose the Apriori-Roaring-Parallel which explores parallelism in shared memory and demands less memory usage during the mining process. In order to achieve this memory usage reduction, the Apriori-Roaring-Parallel method employs compressed bitmap structures to represent the datasets. The results obtained show that the Apriori-Roaring-Parallel method uses memory efficiently when compared to other methods. |
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Repositório Institucional da UNESP |
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Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMPAssociation RulesBitmap CompressionData MiningIdentification of Frequent SetsMining association rules is a process which consists in extracting knowledge from datasets. This is a widely used technique to analyze customer purchasing patterns, and its process is segmented in two main phases: mining frequent sets and formulating association rules. Several approaches were developed for the first phase of the mining process whose main objective was to reduce execution time. However, as all available datasets are very large (Big Data), there is a limitation regarding its application in these new sets due to excessive memory usage. We propose the Apriori-Roaring-Parallel which explores parallelism in shared memory and demands less memory usage during the mining process. In order to achieve this memory usage reduction, the Apriori-Roaring-Parallel method employs compressed bitmap structures to represent the datasets. The results obtained show that the Apriori-Roaring-Parallel method uses memory efficiently when compared to other methods.São Paulo State University UNESP Computing DepartmentSão Paulo State University UNESP Department of Computer Science And Statistics, São José do Rio PretoHumber Institute of Technology And Advanced Learning Faculty of BusinessSão Paulo State University UNESP Computing DepartmentSão Paulo State University UNESP Department of Computer Science And Statistics, São José do Rio PretoUniversidade Estadual Paulista (UNESP)Faculty of BusinessColombo, Alexandre [UNESP]Spolon, Roberta [UNESP]Lobato, Renata Spolon [UNESP]Manacero, Aleardo [UNESP]Cavenaghi, Marcos Antonio2022-04-29T08:38:44Z2022-04-29T08:38:44Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISCC53001.2021.9631495Proceedings - IEEE Symposium on Computers and Communications, v. 2021-September.1530-1346http://hdl.handle.net/11449/23025310.1109/ISCC53001.2021.96314952-s2.0-85123186952Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - IEEE Symposium on Computers and Communicationsinfo:eu-repo/semantics/openAccess2022-04-29T08:38:44Zoai:repositorio.unesp.br:11449/230253Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:03:00.359995Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
title |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
spellingShingle |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP Colombo, Alexandre [UNESP] Association Rules Bitmap Compression Data Mining Identification of Frequent Sets |
title_short |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
title_full |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
title_fullStr |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
title_full_unstemmed |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
title_sort |
Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP |
author |
Colombo, Alexandre [UNESP] |
author_facet |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero, Aleardo [UNESP] Cavenaghi, Marcos Antonio |
author_role |
author |
author2 |
Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero, Aleardo [UNESP] Cavenaghi, Marcos Antonio |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Faculty of Business |
dc.contributor.author.fl_str_mv |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero, Aleardo [UNESP] Cavenaghi, Marcos Antonio |
dc.subject.por.fl_str_mv |
Association Rules Bitmap Compression Data Mining Identification of Frequent Sets |
topic |
Association Rules Bitmap Compression Data Mining Identification of Frequent Sets |
description |
Mining association rules is a process which consists in extracting knowledge from datasets. This is a widely used technique to analyze customer purchasing patterns, and its process is segmented in two main phases: mining frequent sets and formulating association rules. Several approaches were developed for the first phase of the mining process whose main objective was to reduce execution time. However, as all available datasets are very large (Big Data), there is a limitation regarding its application in these new sets due to excessive memory usage. We propose the Apriori-Roaring-Parallel which explores parallelism in shared memory and demands less memory usage during the mining process. In order to achieve this memory usage reduction, the Apriori-Roaring-Parallel method employs compressed bitmap structures to represent the datasets. The results obtained show that the Apriori-Roaring-Parallel method uses memory efficiently when compared to other methods. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-29T08:38:44Z 2022-04-29T08:38:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ISCC53001.2021.9631495 Proceedings - IEEE Symposium on Computers and Communications, v. 2021-September. 1530-1346 http://hdl.handle.net/11449/230253 10.1109/ISCC53001.2021.9631495 2-s2.0-85123186952 |
url |
http://dx.doi.org/10.1109/ISCC53001.2021.9631495 http://hdl.handle.net/11449/230253 |
identifier_str_mv |
Proceedings - IEEE Symposium on Computers and Communications, v. 2021-September. 1530-1346 10.1109/ISCC53001.2021.9631495 2-s2.0-85123186952 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - IEEE Symposium on Computers and Communications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129484650446848 |