  Measurement is a fundamental building block of numerous scientific models and
their creation. This is in particular true for data driven science. Due to the
high complexity and size of modern data sets, the necessity for the development
of understandable and efficient scaling methods is at hand. A profound theory
for scaling data is scale-measures, as developed in the field of formal concept
analysis. Recent developments indicate that the set of all scale-measures for a
given data set constitutes a lattice and does hence allow efficient exploring
algorithms. In this work we study the properties of said lattice and propose a
novel scale-measure exploration algorithm that is based on the well-known and
proven attribute exploration approach. Our results motivate multiple
applications in scale recommendation, most prominently (semi-)automatic
scaling.
