Built-In Scalar Dimension
Scalar1 for instance would be a dimension with predefined values from 0-100. You could assign meaningful names to each value using an alias. The benefit would be that rules requiring a previous or next value would not require a "lookup" for what the previous or next value is. This is important in nested rules. For instance in occupational modelling where next year, next age is a function of previous year, previous age.
NEXT_YEAR, NEXT_AGE = PALO.DATA("XXXDatabase","CCCube",PALO.EPREV("XXXDatabase","XXDimension",!'XXDimension'),PALO.EPREV("XXDatabase","YYDimension",!'YYDimension'),…) * …
This could be replaced with NEXT_YEAR, NEXT_AGE = PALO.DATA("XXXDatabase","CCCube",PREVIOUS("XXDimension"),PREVIOUS("YYDimension"),,…) *
Greg Hermus commented
Just a follow-up. Actually with the built in scalar dimension you would not even require the palo.data function call just the previous("XXDimension"), previous ("YYDimension").