Mobility data warehouses

"The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be u...

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Detalles Bibliográficos
Autores principales: Vaisman, Alejandro Ariel, Zimányi, Esteban
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ALMACENES DE DATOS
ANALISIS DE DATOS
OLAP
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1767
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1767
record_format dspace
spelling I32-R138-123456789-17672022-12-07T13:07:01Z Mobility data warehouses Vaisman, Alejandro Ariel Zimányi, Esteban ALMACENES DE DATOS ANALISIS DE DATOS OLAP "The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB." 2019-09-26T13:19:21Z 2019-09-26T13:19:21Z 2019-04 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion 2220-9964 http://ri.itba.edu.ar/handle/123456789/1767 en info:eu-repo/semantics/reference/doi/10.3390/ijgi8040170 info:eu-repo/grantAgreement/ANPCyT/PICT/2014-0787/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/ANPCyT/PICT/2017-1054/AR. Ciudad Autónoma de Buenos Aires http://creativecommons.org/licenses/by/4.0/ 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 ALMACENES DE DATOS
ANALISIS DE DATOS
OLAP
spellingShingle ALMACENES DE DATOS
ANALISIS DE DATOS
OLAP
Vaisman, Alejandro Ariel
Zimányi, Esteban
Mobility data warehouses
topic_facet ALMACENES DE DATOS
ANALISIS DE DATOS
OLAP
description "The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB."
format Artículos de Publicaciones Periódicas
publishedVersion
author Vaisman, Alejandro Ariel
Zimányi, Esteban
author_facet Vaisman, Alejandro Ariel
Zimányi, Esteban
author_sort Vaisman, Alejandro Ariel
title Mobility data warehouses
title_short Mobility data warehouses
title_full Mobility data warehouses
title_fullStr Mobility data warehouses
title_full_unstemmed Mobility data warehouses
title_sort mobility data warehouses
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1767
work_keys_str_mv AT vaismanalejandroariel mobilitydatawarehouses
AT zimanyiesteban mobilitydatawarehouses
_version_ 1765660760872058880

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