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Constructing a digital twin with Material
Like most Material functions, the digital twin builder must have all its knowledge saved in a lakehouse, with Material instruments defining the ontology that maps knowledge to the real-world techniques and the processes your digital twin will mannequin. Having specialists readily available is necessary as you build the vocabulary that Fabric uses to tug collectively many various knowledge sources and streams. This mapping course of is essential to defining the important entities your mannequin makes use of, bringing them along with queries to outline semantic relationships.
Entities are particular machines, processes, inputs, and outputs, in addition to the folks and techniques that sit round them. These are linked to the information in Material related to them in a semantic hierarchy that permits you to wrap totally different entities collectively.
After getting constructed this map of your knowledge, you’re ready to make use of different Material instruments to discover and analyze the information, linking it to the techniques and the processes you’re modeling. This may be prolonged additional, out into AI instruments for extra insights, for instance, discovering outliers in your knowledge or offering visualizations. APIs allow you to use that knowledge with code, linking it to digital twins of your management techniques and fashions of bodily {hardware}. Visualizations may be in-built acquainted instruments, corresponding to Energy BI, or knowledge may be delivered.
