FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases

Autores
Cabrera Rosero, Omar Ernesto; Calderón Romero, Andrés Oswaldo
Año de publicación
2018
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
flock patterns
frequent patterns mining
spatio-temporal databases
movement patterns
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/70636

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spelling FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databasesCabrera Rosero, Omar ErnestoCalderón Romero, Andrés OswaldoCiencias Informáticasflock patternsfrequent patterns miningspatio-temporal databasesmovement patternsThe widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.Sociedad Argentina de Informática e Investigación Operativa2018-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1-14http://sedici.unlp.edu.ar/handle/10915/70636spainfo:eu-repo/semantics/altIdentifier/url/http://47jaiio.sadio.org.ar/sites/default/files/AGRANDA-01.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7569info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:03:20Zoai:sedici.unlp.edu.ar:10915/70636Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:03:20.937SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
title FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
spellingShingle FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
Cabrera Rosero, Omar Ernesto
Ciencias Informáticas
flock patterns
frequent patterns mining
spatio-temporal databases
movement patterns
title_short FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
title_full FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
title_fullStr FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
title_full_unstemmed FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
title_sort FP-Flock: An algorithm to find frequent flock patterns in spatio-temporal databases
dc.creator.none.fl_str_mv Cabrera Rosero, Omar Ernesto
Calderón Romero, Andrés Oswaldo
author Cabrera Rosero, Omar Ernesto
author_facet Cabrera Rosero, Omar Ernesto
Calderón Romero, Andrés Oswaldo
author_role author
author2 Calderón Romero, Andrés Oswaldo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
flock patterns
frequent patterns mining
spatio-temporal databases
movement patterns
topic Ciencias Informáticas
flock patterns
frequent patterns mining
spatio-temporal databases
movement patterns
dc.description.none.fl_txt_mv The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.
Sociedad Argentina de Informática e Investigación Operativa
description The widespread use of location systems such as GPS and RFID along with the massive use of mobile devices have allowed a significant increase in the availability and access to spatio-temporal databases in recent years. This large amount of data has motivated the development of more efficient techniques to process queries about the behavior of moving objects, like discovering behavior patterns among trajectories of moving objects over a continuous period of time. Several studies have focused on the query patterns that capture the behavior of entities in motion, which are reflected in collaborations such as mobile clusters, convoy queries and flock patterns. In this paper, we provided an algorithm to find clustering patterns, traditionally known as flocks, which is based on a frequent pattern mining approach. Twoalternatives for detecting patterns, both online and offline, are presented. Both alternatives were compared with two algorithms of the same type, Basic Flock Evaluation (BFE) and LCMFlock. The performance and behavior was measured in different datasets, both synthetic and real.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
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