Analysing River Systems with Time Series Data Using Path Queries in Graph Databases

Abstract

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.

Temporal Paths in Real-World Sensor Networks

“…The temporal graph model that we use to represent sensor networks considers different notions of temporal paths that account for different situations that may occur in such a network. These paths have been studied in [5,8,9] and are denoted as continuous, pairwise continuous, consecutive and flow paths. As an example, a continuous path (CP) is a path in the network graph that is continuously valid during a certain time interval such that the water temperature was continuously over ten degrees Celsius between 10 June 2023 and 12 June 2023; that is, these paths are defined in terms of the network topology and certain conditions over the time-series data.…”

“…This situation can be modeled as a temporal graph with time series attached to the graph nodes. A first model for this was proposed in [7], and its follow-up work was presented in [5]. Both works consider time series of categorical data values; therefore, measurements are categorized before being loaded into the database.…”

“…Both works consider time series of categorical data values; therefore, measurements are categorized before being loaded into the database. The work in [5] applies the model to a real-world case, namely a portion of the Yser river in the Flanders' river system in Belgium. The underlying idea is that temporal paths capture interesting situations in the river flow, allowing experts to detect situations of interest.…”

“…The work in [5] proposes representing and storing transportation networks using temporal graph databases and querying them with high-level temporal graph query languages. The underlying idea is incrementally built as explained next.…”

“…Therefore, a static graph does not suffice to represent such a situation; therefore, we need a temporal graph. As a solution to this problem, the model in [5] was defined. In the temporal property graph data model used in that paper, nodes and edges are labeled with validity intervals that indicate the period when a node, an edge or a property existed in the graph, thus satisfying the requirements mentioned above.…”