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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/70636
Ver los metadatos del registro completo
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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 |
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2018-09 |
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