Ponencia en Congreso: Modeling and querying sensor networks using temporal graph databases
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2022
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"Transportation networks (e.g., river systems or road net works) equipped with sensors that collect data for several different pur poses can be naturally modeled using graph databases. However, since networks can change over time, to represent these changes appropriately, a temporal graph data model is required. In this paper, we show that sensor-equipped transportation networks can be represented and queried using temporal graph databases and query languages. For this, 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. We
redefine temporal paths and study and implement a new kind of path, called Flow path. We take the Flanders’ river system as a use case."
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BASES DE DATOS ORIENTADAS A GRAFOS