Analytical queries on semantic trajectories using graph databases

"This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analy...

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Detalles Bibliográficos
Autores principales: Gómez, Leticia Irene, Kuijpers, Bart, Vaisman, Alejandro Ariel
Formato: Artículos de Publicaciones Periódicas acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
BASES DE DATOS ORIENTADAS A GRAFOS
ANALISIS DE DATOS
LENGUAJES DE CONSULTA
GPS
GIS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1870
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1870
record_format dspace
spelling I32-R138-123456789-18702022-12-07T13:06:52Z Analytical queries on semantic trajectories using graph databases Gómez, Leticia Irene Kuijpers, Bart Vaisman, Alejandro Ariel BASES DE DATOS ORIENTADAS A GRAFOS ANALISIS DE DATOS LENGUAJES DE CONSULTA GPS GIS "This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non-spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real-world public data case study, the article shows that trajectory queries are expressed more naturally on the graph-based representation than over the relational alternative, and perform better in many typical cases." 2020-01-23T14:45:46Z 2020-01-23T14:45:46Z 2019-10 Artículos de Publicaciones Periódicas info:eu-repo/semantics/acceptedVersion 1361-1682 http://ri.itba.edu.ar/handle/123456789/1870 en info:eu-repo/semantics/altIdentifier/doi/10.1111/tgis.12556 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic BASES DE DATOS ORIENTADAS A GRAFOS
ANALISIS DE DATOS
LENGUAJES DE CONSULTA
GPS
GIS
spellingShingle BASES DE DATOS ORIENTADAS A GRAFOS
ANALISIS DE DATOS
LENGUAJES DE CONSULTA
GPS
GIS
Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
Analytical queries on semantic trajectories using graph databases
topic_facet BASES DE DATOS ORIENTADAS A GRAFOS
ANALISIS DE DATOS
LENGUAJES DE CONSULTA
GPS
GIS
description "This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non-spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real-world public data case study, the article shows that trajectory queries are expressed more naturally on the graph-based representation than over the relational alternative, and perform better in many typical cases."
format Artículos de Publicaciones Periódicas
acceptedVersion
author Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
author_facet Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
author_sort Gómez, Leticia Irene
title Analytical queries on semantic trajectories using graph databases
title_short Analytical queries on semantic trajectories using graph databases
title_full Analytical queries on semantic trajectories using graph databases
title_fullStr Analytical queries on semantic trajectories using graph databases
title_full_unstemmed Analytical queries on semantic trajectories using graph databases
title_sort analytical queries on semantic trajectories using graph databases
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/1870
work_keys_str_mv AT gomezleticiairene analyticalqueriesonsemantictrajectoriesusinggraphdatabases
AT kuijpersbart analyticalqueriesonsemantictrajectoriesusinggraphdatabases
AT vaismanalejandroariel analyticalqueriesonsemantictrajectoriesusinggraphdatabases
_version_ 1765660763944386560

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