For groups of data that don't change often, we can compute statistics on insert, such as \texttt{sum}, \texttt{avg}, \texttt{min}, \texttt{max}, etc.
These aggregates can be used to check whether data needed is within that group.
This also enable computing (some) aggregates without reading the entire table and is commonly used in data mining and data cubes.

A concept that is not used in DB engines, is count min sketch. It can be very useful in data streaming engines and large scale data processing.
What this does is it computes statistics on how many times a given value was seen in a list or stream of data and is a variation of the bloom filter,
but with several arrays and counting. The overestimation in count is bounded by the size in the matrix.
