  We present a novel data-driven reformulation of the iterative SVD-rational
Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin
(two-sided) projection-based iterative method for model reduction combining
rational Krylov subspaces (on one side) with Gramian/SVD based subspaces (on
the other side). We show that in each step of ISRK, we do not necessarily
require access to the original system matrices, but only to input/output data
in the form of the system's transfer function, evaluated at particular values
(frequencies). Numerical examples illustrate the efficiency of the new
data-driven formulation.
