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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/3/94 |
| _version_ | 1797611397684658176 |
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| author | Erik Bollen Rik Hendrix Bart Kuijpers Valeria Soliani Alejandro Vaisman |
| author_facet | Erik Bollen Rik Hendrix Bart Kuijpers Valeria Soliani Alejandro Vaisman |
| author_sort | Erik Bollen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T06:28:20Z |
| format | Article |
| id | doaj.art-a91b71bcb75640afa897ea19ede666e1 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T06:28:20Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-a91b71bcb75640afa897ea19ede666e12023-11-17T11:27:59ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-02-011239410.3390/ijgi12030094Analysing River Systems with Time Series Data Using Path Queries in Graph DatabasesErik Bollen0Rik Hendrix1Bart Kuijpers2Valeria Soliani3Alejandro Vaisman4Databases and Theoretical Computer Science Group, Data Science Institute (DSI), Hasselt University and transnational University Limburg, 3500 Hasselt, BelgiumFlemish Institute for Technological Research (VITO), 2400 Mol, BelgiumDatabases and Theoretical Computer Science Group, Data Science Institute (DSI), Hasselt University and transnational University Limburg, 3500 Hasselt, BelgiumDatabases and Theoretical Computer Science Group, Data Science Institute (DSI), Hasselt University and transnational University Limburg, 3500 Hasselt, BelgiumInstituto Tecnológico de Buenos Aires, Buenos Aires C1437, ArgentinaTransportation 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.https://www.mdpi.com/2220-9964/12/3/94river systemstransportation networkssensor networksgraph databasestemporal databasestemporal query languages |
| spellingShingle | Erik Bollen Rik Hendrix Bart Kuijpers Valeria Soliani Alejandro Vaisman Analysing River Systems with Time Series Data Using Path Queries in Graph Databases ISPRS International Journal of Geo-Information river systems transportation networks sensor networks graph databases temporal databases temporal query languages |
| title | 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_short | 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 |
| topic | river systems transportation networks sensor networks graph databases temporal databases temporal query languages |
| url | https://www.mdpi.com/2220-9964/12/3/94 |
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