Analysing river systems with time series data using path queries in graph databases

Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to repr...

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Autores principales: Bollen, Erik, Hendrix, Rik, Kuijpers, Bart, Soliani, Valeria, Vaisman, Alejandro
Formato: Artículo de publicación periódica
Lenguaje:Inglés
Publicado: 2024
Materias:
SISTEMAS FLUVIALES
REDES DE TRANSPORTE
REDES DE SENSORES
BASES DE DATOS DE GRÁFICOS
BASES DE DATOS TEMPORALES
LENGUAJES DE CONSULTA TEMPORALES
Acceso en línea:https://ri.itba.edu.ar/handle/20.500.14769/4460
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-4460
record_format dspace
spelling I32-R138-20.500.14769-44602026-01-15T14:37:59Z Analysing river systems with time series data using path queries in graph databases Bollen, Erik Hendrix, Rik Kuijpers, Bart Soliani, Valeria Vaisman, Alejandro SISTEMAS FLUVIALES REDES DE TRANSPORTE REDES DE SENSORES BASES DE DATOS DE GRÁFICOS BASES DE DATOS TEMPORALES LENGUAJES DE CONSULTA TEMPORALES Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In this model, time-series data are represented as properties of nodes in the network, and nodes and edges are timestamped with their validity intervals. In this paper, we show that transportation networks can be represented and queried using temporal graph databases and temporal graph query languages. Many interesting situations can be captured by the temporal paths supported by this model. To achieve the above, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph, redefine temporal paths and study and implement new kinds of paths, namely Flow paths and Backwards Flow paths. Further, we analyze a real-world case, using a portion of the Yser river in the Flanders’ river system in Belgium, where some nodes are equipped with sensors while other ones are not. We model this river as a temporal graph, implement it using real data provided by the sensors, and discover interesting temporal paths based on the electric conductivity parameter, that can be used in a decision support environment, by experts for analyzing water quality across time. 2024-05-07T15:16:00Z 2024-05-07T15:16:00Z 2023 Artículo de publicación periódica https://ri.itba.edu.ar/handle/20.500.14769/4460 en 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 SISTEMAS FLUVIALES
REDES DE TRANSPORTE
REDES DE SENSORES
BASES DE DATOS DE GRÁFICOS
BASES DE DATOS TEMPORALES
LENGUAJES DE CONSULTA TEMPORALES
spellingShingle SISTEMAS FLUVIALES
REDES DE TRANSPORTE
REDES DE SENSORES
BASES DE DATOS DE GRÁFICOS
BASES DE DATOS TEMPORALES
LENGUAJES DE CONSULTA TEMPORALES
Bollen, Erik
Hendrix, Rik
Kuijpers, Bart
Soliani, Valeria
Vaisman, Alejandro
Analysing river systems with time series data using path queries in graph databases
topic_facet SISTEMAS FLUVIALES
REDES DE TRANSPORTE
REDES DE SENSORES
BASES DE DATOS DE GRÁFICOS
BASES DE DATOS TEMPORALES
LENGUAJES DE CONSULTA TEMPORALES
description Transportation networks are used in many application areas, like traffic control or river monitoring. For this purpose, sensors are placed in strategic points in the network and they send their data to a central location for storage, viewing and analysis. Recent work proposed graph databases to represent transportation networks, since these networks can change over time, a temporal graph data model is required to keep track of these changes. In this model, time-series data are represented as properties of nodes in the network, and nodes and edges are timestamped with their validity intervals. In this paper, we show that transportation networks can be represented and queried using temporal graph databases and temporal graph query languages. Many interesting situations can be captured by the temporal paths supported by this model. To achieve the above, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph, redefine temporal paths and study and implement new kinds of paths, namely Flow paths and Backwards Flow paths. Further, we analyze a real-world case, using a portion of the Yser river in the Flanders’ river system in Belgium, where some nodes are equipped with sensors while other ones are not. We model this river as a temporal graph, implement it using real data provided by the sensors, and discover interesting temporal paths based on the electric conductivity parameter, that can be used in a decision support environment, by experts for analyzing water quality across time.
format Artículo de publicación periódica
author Bollen, Erik
Hendrix, Rik
Kuijpers, Bart
Soliani, Valeria
Vaisman, Alejandro
author_facet Bollen, Erik
Hendrix, Rik
Kuijpers, Bart
Soliani, Valeria
Vaisman, Alejandro
author_sort Bollen, Erik
title Analysing river systems with time series data using path queries in graph databases
title_short Analysing river systems with time series data using path queries in graph databases
title_full Analysing river systems with time series data using path queries in graph databases
title_fullStr Analysing river systems with time series data using path queries in graph databases
title_full_unstemmed Analysing river systems with time series data using path queries in graph databases
title_sort analysing river systems with time series data using path queries in graph databases
publishDate 2024
url https://ri.itba.edu.ar/handle/20.500.14769/4460
work_keys_str_mv AT bollenerik analysingriversystemswithtimeseriesdatausingpathqueriesingraphdatabases
AT hendrixrik analysingriversystemswithtimeseriesdatausingpathqueriesingraphdatabases
AT kuijpersbart analysingriversystemswithtimeseriesdatausingpathqueriesingraphdatabases
AT solianivaleria analysingriversystemswithtimeseriesdatausingpathqueriesingraphdatabases
AT vaismanalejandro analysingriversystemswithtimeseriesdatausingpathqueriesingraphdatabases
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