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Materialize Data (RapidMiner Studio Core)
SynopsisThis operator creates a fresh and clean copy of the data in the memory.
The Materialize Data operator creates a fresh and clean copy of the data in the memory. It might be useful after large preprocessing chains with a lot of views or even data copies. In such cases, it can be especially useful in combination with a memory cleanup operator e.g. the Free Memory operator.
- example set input (Data Table)
This input port expects an ExampleSet. It is the output of the Subprocess operator in the attached Example Process. The output of other operators can also be used as input.
- example set output (Data Table)
The fresh and clean copy of the ExampleSet is delivered through this port.
- original (Data Table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
- datamanagementThis is an expert parameter. There are different options, users can choose any of them Range: selection
Creating fresh copy of an ExampleSet
This is a very simple Example Process which just shows how to use the Materialize Data operator. The 'Labor-Negotiations' data set is loaded using the Retrieve operator. The Subprocess operator is applied on it. No operator is applied in the Subprocess operator because it is used as a dummy operator here. Suppose we have large preprocessing chains with a lot of views or even data copies in the subprocess and we want a fresh copy of data after the subprocess is complete. The Materialize Data operator is applied after the Subprocess operator to create a fresh and clean copy of the data. No large preprocessing tasks were performed in this Example Process because this Process was intended to discuss only the way this operator can be applied.