When are most functions applied in the context of time and space aggregation?

In the context of time and space aggregation, most functions are typically applied after the aggregation has occurred. This approach allows for the analysis and transformation of the already aggregated data to yield insights or perform calculations based on the resultant sets, rather than on the raw data.

In many data processing frameworks, functions that summarize or manipulate data—such as averages, sums, or standard deviations—are usually conducted on the aggregated outputs. This is crucial because it ensures that the functions are applied to a compact representation of the data, enabling more efficient processing and meaningful interpretations.

The implementation of functions before aggregation could lead to distorted results since the raw data would not yet reflect the summarized nature that aggregation provides. Similarly, applying functions during aggregation would impede the function of the aggregation process itself, which relies on combining data points in a meaningful way before any further calculations are made. Starting at the process should not be a focus for function application, because the data needs to be in an aggregated form to derive valuable insights from function application.