Analysing River Systems with Time Series Data Using Path Queries in Graph Databases
Bibliographic Data
| ID | 22033997 |
|---|---|
| Authors | Erik Bollen (0000-0002-9287-1094, Flemish Institute for Technological Research), Rik Hendrix (0000-0002-1572-1279, Flemish Institute for Technological Research), Bart Kuijpers (0000-0001-5774-0948, Hasselt University, corresponding author), Valeria Soliani (0000-0003-2284-1137, Instituto Tecnológico de Buenos Aires (ITBA)), Alejandro Vaisman (0000-0002-3945-4187, Instituto Tecnológico de Buenos Aires (ITBA)) |
| Year | 2023 |
| Volume | 12 |
| Issue | 3 |
| Pages | 94 |
| Publication date | 2023-02-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12030094 |
| OpenAlex | W4322500416 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
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
Data mining · Database · Flow network · Graph · Graph database · Temporal database · Computer Science · Data Management and Algorithms · Graph Theory and Algorithms · Traffic Prediction and Management Techniques · Theoretical Computer Science
| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |