Managing data of sensor-equipped transportation networks using graph databases

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Publikašuvnnas:Geoscientific Instrumentation, Methods and Data Systems vol. 13, no. 2 (2024), p. 353
Váldodahkki: Bollen, Erik
Eará dahkkit: Hendrix, Rik, Kuijpers, Bart
Almmustuhtton:
Copernicus GmbH
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100 1 |a Bollen, Erik  |u Databases and Theoretical Computer Science Group, Data Science Institute (DSI), Hasselt University and transnational University Limburg, Agoralaan building D Diepenbeek 3590, Belgium; Data Science Hub, VITO, Boeretang 200 Mol 2400, Belgium 
245 1 |a Managing data of sensor-equipped transportation networks using graph databases 
260 |b Copernicus GmbH  |c 2024 
513 |a Journal Article 
520 3 |a In this paper, we are concerned with data pertinent to transportation networks, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with sensors that monitor the network and the objects moving along it. These sensors produce time series data, resulting in sensor networks. Examples are river, road, and electricity networks.Geographical information systems are used to gather, store, and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time series and measurement patterns may be combined with graph patterns to describe, retrieve, and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin. 
653 |a Language 
653 |a Databases 
653 |a River basins 
653 |a Information systems 
653 |a Internet 
653 |a Application programming interface 
653 |a Graphs 
653 |a Sensors 
653 |a Transportation networks 
653 |a Rivers 
653 |a Time series 
653 |a Geographic information systems 
653 |a Time measurement 
653 |a Data analysis 
653 |a Computer programs 
653 |a Spatial data 
653 |a Hydrology 
653 |a Graph theory 
653 |a Resource Description Framework-RDF 
653 |a Queries 
653 |a Hydrologic research 
653 |a River networks 
653 |a Environmental 
700 1 |a Hendrix, Rik  |u Data Science Hub, VITO, Boeretang 200 Mol 2400, Belgium 
700 1 |a Kuijpers, Bart  |u Databases and Theoretical Computer Science Group, Data Science Institute (DSI), Hasselt University and transnational University Limburg, Agoralaan building D Diepenbeek 3590, Belgium 
773 0 |t Geoscientific Instrumentation, Methods and Data Systems  |g vol. 13, no. 2 (2024), p. 353 
786 0 |d ProQuest  |t Publicly Available Content Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3133173512/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
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