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http://hdl.handle.net/1942/38915
Title: | Modeling and Querying Sensor Networks Using Temporal Graph Databases | Authors: | KUIJPERS, Bart SOLIANI, Valeria VAISMAN, Alejandro |
Issue Date: | 2022 | Publisher: | Springer | Source: | Chiusano, Silvia; Cerquitelli, Tania; Wrembel, Robert; Norvag, Kjetil; Catania, Barbara (Ed.). ADBIS 2022: New Trends in Database and Information Systems, Springer, p. 222 -231 | Series/Report: | Communications in Computer and Information Science | Series/Report no.: | 1652 | Abstract: | Transportation networks (e.g., river systems or road networks) equipped with sensors that collect data for several different purposes 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. | Keywords: | Graph databases;Temporal databases;Sensor networks | Document URI: | http://hdl.handle.net/1942/38915 | ISBN: | 978-3-031-15742-4 | DOI: | 10.1007/978-3-031-15743-1_21 | ISI #: | 000892609000021 | Datasets of the publication: | https://doi.org/10.1007/978-3-031-15743-1_21 | Rights: | 2022 Springer Nature Switzerland AG | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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978-3-031-15743-1_21.pdf Restricted Access | Published version | 2.29 MB | Adobe PDF | View/Open Request a copy |
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