Constructing a digital twin with Material
Like most Material purposes, the digital twin builder must have all its information saved in a lakehouse, with Material instruments defining the ontology that maps information to the real-world techniques and the processes your digital twin will mannequin. Having consultants readily available is necessary as you construct the vocabulary that Material makes use of to drag collectively many alternative information sources and streams. This mapping course of is vital 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 info in Material related to them in a semantic hierarchy that allows you to wrap totally different entities collectively.
Upon getting constructed this map of your information, you’re in a position to make use of different Material instruments to discover and analyze the info, 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 information or offering visualizations. APIs allow you to use that information with code, linking it to digital twins of your management techniques and fashions of bodily {hardware}. Visualizations may be in-built acquainted instruments, reminiscent of Energy BI, or information may be delivered.
