Modeling and Querying Sensor Networks Using Temporal Graph Databases

Kuijpers, Bart; Soliani, Valeria; Vaisman, Alejandro

doi:10.1007/978-3-031-15743-1_21

Cited by 2 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Contributionsmentioning

confidence: 99%

“…Different semantics for these paths are studied, raising the notion of continuous, pairwise continuous and consecutive paths. Kuijpers et al [9] extended this model, allowing time series to be defined as node properties. The values in these time series are used to redefine the paths mentioned above.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 3 shows a sensor network (based on the river fragment of Figure 2), where the sets of sensor nodes are S = {1, 4, 8} and their times series, measuring water temperature (in degrees Celsius) at regular moments, are ts(1) = {(1, 12), (2,10), (3,8), (4,10), (5,12), (6,9), (7, 8.5), (8,8), (9, 10)}; ts(4) = {(1, 9), (2,10), (3,9), (4,10), (5,12), (6,9), (7,10), (8,11), (9,11)}; and (2,8), (3,10), (4,10), (5,9), (6,10), (7,11), (8,11), (9,12)}.…”

mentioning

confidence: 99%

“…n e L Z S 6 9 F 6 s V 5 n r Q X r e 2 Y f / s B 6 + w K x Z J U 9 < / l a t e x i t > ts < l a t e x i t s h a 1 _ b a s e 6 4 = " N y 5 S x i x q 6 s / S t v n h y s p {(1, 9), (2, 10), (3,9), (4, 10), (5,12), (6,9), (7,10), (8,11), (9,11)} < l a t e x i t s h a 1 _ b a s e 6 4 = " h 8 4 (2,10), (3,8), (4,10), (5,12), (6,9), (7, 8.5), (8,8), (9, 10)} < l a t e x i t s h a 1 _ b a s e 6 4 = " 8 6 s 4 (2,8), (3,10), (4, 10), (5,9), (6, 10), (7,11), (8,11), (9, 12)} Remark 1. In practical applications, we will usually have T = R and V = R, but the sets T and V can also be finite.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Temporal Paths in Real-World Sensor Networks

Bollen,

Kuijpers,

Soliani

et al. 2024

IJGI

View full text Add to dashboard Cite

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.

show abstract

Section: Contributionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Temporal Paths in Real-World Sensor Networks

Bollen,

Kuijpers,

Soliani

et al. 2024

IJGI

View full text Add to dashboard Cite

show abstract

“…In our case, we use categorical time series mentioned above. A first version of this extension appeared in an extended abstract [24]. Well-known concepts in temporal databases are valid and transaction times [6].…”

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

View full text Add to dashboard Cite

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.

show abstract

Modeling and Querying Sensor Networks Using Temporal Graph Databases

Cited by 2 publications

References 5 publications

Temporal Paths in Real-World Sensor Networks

Temporal Paths in Real-World Sensor Networks

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

Contact Info

Product

Resources

About