Indexing Continuous Paths in Temporal Graphs

Kuijpers, Bart; Ribas, Ignacio; Soliani, Valeria; Vaisman, Alejandro

doi:10.1007/978-3-031-15743-1_22

Cited by 3 publications

(2 citation statements)

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“…The values in these time series are used to redefine the paths mentioned above. Further, in [15], the authors introduce methods for indexing temporal graph databases. Since this model is used in the case study presented in Section 7, we give a comprehensive overview in Section 6.…”

Section: Related Workmentioning

confidence: 99%

Temporal Paths in Real-World Sensor Networks

Bollen,

Kuijpers,

Soliani

et al. 2024

IJGI

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Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to take informed and timely decisions on their operation. Temporal paths are first-class citizens in this model and some classes of them have been identified in the literature. Queries aimed at finding these paths are denoted as (temporal) path queries. In spite of these efforts, many interesting problems remain open and, in this work, we aim at answering some of them. More concretely, we characterize the classes of temporal paths that can be defined in a sensor network in terms of the well-known Allen’s temporal algebra. We also show that, out of the 8192 possible interval relations in this algebra, only 11 satisfy two desirable properties that we define: transitivity and robustness. We show how these properties and the paths that satisfy them are relevant in practice by means of a real-world use case consisting of an analysis of salinity that appears close to the Scheldt river in Flanders, Belgium, during high tides occurring in the North Sea.

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Section: Related Workmentioning

confidence: 99%

Temporal Paths in Real-World Sensor Networks

Bollen,

Kuijpers,

Soliani

et al. 2024

IJGI

Self Cite

View full text Add to dashboard Cite

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“…The model introduced in the paper mentioned above is substantially expanded here, also with the introduction of new relevant kinds of paths also including the possibility to define patterns in the paths, like a variable continuously increasing or decreasing in forward or backward (i.e., against the flow) direction. Also, in [25] the authors introduce novel methods for indexing temporal property graph databases.…”

Section: Related Workmentioning

confidence: 99%

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

Bollen

Hendrix

Kuijpers

et al. 2023

IJGI

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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.

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