*One *of the uses presamples is to use aggregate data to simplify models.
The burger paper was an example of how to aggregate data at the LCI level (aggregated LCI datasets, i.e. BA-1s)
Data can be aggregated on many other levels:
at the indicator score level (lightest possible weight of data in an LCA, least resolution)
at the level of parameters that are themselves calculated from external models and that are then used as input parameters to the LCA model. Here, we are aggregating the parameter values and model structure of everything before the parameter value(s)
as supply vectors
The proposal is to extract what is common to all these cases and then adapt (subclass) for different aggregation levels.
Potential common methods:
1) aggregating: calculating results once
2) External transformation functions during aggregation (specific example: balancing of land use of water flows)
3) saving result arrays
4) determine history/pedigree of aggregated dataset (store ancestry somehow - see Bonsai's work? blockchain?)
5) supplanting of model "branches" by aggregate "leaves" and (if possible) vice-versa
*One *of the uses presamples is to use aggregate data to simplify models. The burger paper was an example of how to aggregate data at the LCI level (aggregated LCI datasets, i.e. BA-1s) Data can be aggregated on many other levels:
The proposal is to extract what is common to all these cases and then adapt (subclass) for different aggregation levels.
Potential common methods:
1) aggregating: calculating results once 2) External transformation functions during aggregation (specific example: balancing of land use of water flows) 3) saving result arrays 4) determine history/pedigree of aggregated dataset (store ancestry somehow - see Bonsai's work? blockchain?)
5) supplanting of model "branches" by aggregate "leaves" and (if possible) vice-versa
Link to be made to temporalis and acyclic trees