  The ordinal approach to evaluate time series due to innovative works of Bandt
and Pompe has increasingly established itself among other techniques of
nonlinear time series analysis. In this paper, we summarize and generalize the
theory of determining the Kolmogorov-Sinai entropy of a measure-preserving
dynamical system via increasing sequences of order generated partitions of the
state space. Our main focus are measuring processes without information loss.
Particularly, we consider the question of the minimal necessary number of
measurements related to the properties of a given dynamical system.
