Data Aggregation is the process where multiple data elements are gathered and expressed in a summary form to build a new data element. The steps involved in the aggregation process include reading the raw data, transforming it though certain rules and presenting the summarized form. Rules vary widely in complexity from simple arithmetic sums to complex probabilistic aggregations. In general two types of aggregations exist: time aggregation (daily, weekly, monthly, etc.) and spatial or hierarchical aggregation (where time is not applicable or does not change, e.g. daily well production data to daily field production data or subsidiary A assets to holding company B assets) Individual data elements are associated with either Input Variables or Calculated Results. Time-based aggregations will always result in an aggregated Input Variable or Calculated Result that has the same name as that of the source data, only at a coarser time scale. Spatial aggregations will usually result in aggregated Input Variables or Calculated Results that have the same name as that of the source data, but not always. In some cases, the aggregated Input Variable or Calculated Result variable name may be different from that of the source being aggregated.