Graph Data Models

Author(s):  
Claudio Gutierrez ◽  
Jan Hidders ◽  
Peter T. Wood
Keyword(s):  
2018 ◽  
Vol 11 (12) ◽  
pp. 2106-2109 ◽  
Author(s):  
Alin Deutsch ◽  
Yannis Papakonstantinou

Author(s):  
Maria Constanza Pabon ◽  
Guillermo Andres Montoya ◽  
Martha Millan

Author(s):  
Vito Giovanni Castellana ◽  
Marco Minutoli ◽  
Shreyansh Bhatt ◽  
Khushbu Agarwal ◽  
Arthur Bleeker ◽  
...  

Author(s):  
Claudio Gutiérrez ◽  
Jan Hidders ◽  
Peter T. Wood
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Luka Gradišar ◽  
Matevž Dolenc

An efficient database management system that supports the integration and interoperability of different information models is a foundation on which the higher levels of cyber-physical systems are built. In this paper, we address the problem of integrating monitoring data with building information models through the use of the graph data management system and the IFC standard (Industry Foundation Classes) to support the need for interoperability and collaborative work. The proposed workflow describes the conversion of IFC models into a graph database and the connection with data from sensors, which is then validated using the example of a bridge monitoring system. The presented IFC and sensor graph data models are structurally flexible and scalable to meet the challenges of smart cities and big data.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Mona Lundin

This study explores the use of a new protocol in hypertension care, in which continuous patient-generated data reported through digital technology are presented in graphical form and discussed in follow-up consultations with nurses. This protocol is part of an infrastructure design project in which patients and medical professionals are co-designers. The approach used for the study was interaction analysis, which rendered possible detailed in situ examination of local variations in how nurses relate to the protocol. The findings show three distinct engagements: (1) teasing out an average blood pressure, (2) working around the protocol and graph data and (3) delivering an analysis. It was discovered that the graphical representations structured the consultations to a great extent, and that nurses mostly referred to graphs that showed blood pressure values, which is a measurement central to the medical discourse of hypertension. However, it was also found that analysis of the data alone was not sufficient to engage patients: nurses' invisible and inclusion work through eliciting patients' narratives played an important role here. A conclusion of the study is that nurses and patients both need to be more thoroughly introduced to using protocols based on graphs for more productive consultations to be established. 


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