A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Unleashing the Potential of Graph Database in Smart Asset Management: Enhancing Predictive Maintenance in Industry 4.0
The integration of predictive maintenance with Industry 4.0 technologies such as big data, the Internet of Things, and artificial intelligence has led to the possibility of the overwhelming high amounts of data together with the production of unstructured and structured data. Although varieties of analytics will be able to be conducted due to additional information obtained with the state-of-the-art technologies, the capability of managing these high amounts of data is concerning. An agile database is required to accommodate this dynamic digital environment that will help in sustaining the data as reliable data sources for future analytics of intelligent asset management. A graph database is known for its flexibility and scalability due to their schema-less and unfixed structures which makes it easier to add new data. Besides that, its unique structures that represent entities in the form of nodes and relationships in the form of edges have made it excels in dealing with complex join-style queries. This paper discussed the possibility of implementing a graph database as an agile database approach by storing asset information to provide reliable data sources for the predictive maintenance process that will help to realise smart asset management.
Unleashing the Potential of Graph Database in Smart Asset Management: Enhancing Predictive Maintenance in Industry 4.0
The integration of predictive maintenance with Industry 4.0 technologies such as big data, the Internet of Things, and artificial intelligence has led to the possibility of the overwhelming high amounts of data together with the production of unstructured and structured data. Although varieties of analytics will be able to be conducted due to additional information obtained with the state-of-the-art technologies, the capability of managing these high amounts of data is concerning. An agile database is required to accommodate this dynamic digital environment that will help in sustaining the data as reliable data sources for future analytics of intelligent asset management. A graph database is known for its flexibility and scalability due to their schema-less and unfixed structures which makes it easier to add new data. Besides that, its unique structures that represent entities in the form of nodes and relationships in the form of edges have made it excels in dealing with complex join-style queries. This paper discussed the possibility of implementing a graph database as an agile database approach by storing asset information to provide reliable data sources for the predictive maintenance process that will help to realise smart asset management.
Unleashing the Potential of Graph Database in Smart Asset Management: Enhancing Predictive Maintenance in Industry 4.0
Lect. Notes in Networks, Syst.
Ben Ahmed, Mohamed (editor) / Boudhir, Anouar Abdelhakim (editor) / El Meouche, Rani (editor) / Karaș, İsmail Rakıp (editor) / Hairuddin, Farah Ilyana (author) / Azri, Suhaibah (author) / Ujang, Uznir (author)
The Proceedings of the International Conference on Smart City Applications ; 2023 ; Paris, France
2024-02-20
11 pages
Article/Chapter (Book)
Electronic Resource
English
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