Scoring Large ESA Models

Optimize performance by adjusting the System Global Area (SGA) to accommodate large ESA models, ensuring efficient model scoring.

Building an Explicit Semantic Analysis (ESA) model on a large collection of text documents can result in a model with many features or titles. The model information for scoring is loaded into SGA as a shared (shared pool size) library cache object. Different SQL predictive queries can reference this object. When the model size is large, it is necessary to set the SGA parameter in the database to a sufficient size that accommodates large objects. If the SGA is too small, the model may need to be re-loaded every time it is referenced which is likely to lead to performance degradation.