Software Example: Promote a data object to higher levels in a single index tree. For this example, I choose the context of spatial data in processing technology and data management. Non-uniformity in data extents is a general characteristic of spatial data. Indexing such non-uniform data using conventional spatial index structures such as R-trees is inefficient for two reasons: (1) the non-uniformity increases the likelihood of overlapping index entries, and, (2) clustering of non-uniform data is likely to index more dead space than clustering of uniform data. Using the TRIZ-way, we must look in our existing environment for fulfillment of useful functionality. To make the impact of these anomalies more “useful”, we invent a new scheme that promotes data objects to higher levels in tree-based index structures; these object then fulfill different functions based on the positional context in the index tree.