  Granular operator spaces and variants had been introduced and used in
theoretical investigations on the foundations of general rough sets by the
present author over the last few years. In this research, higher order versions
of these are presented uniformly as partial algebraic systems. They are also
adapted for practical applications when the data is representable by data
table-like structures according to a minimalist schema for avoiding
contamination. Issues relating to valuations used in information systems or
tables are also addressed. The concept of contamination introduced and studied
by the present author across a number of her papers, concerns mixing up of
information across semantic domains (or domains of discourse). Rough inclusion
functions (\textsf{RIF}s), variants, and numeric functions often have a direct
or indirect role in contaminating algorithms. Some solutions that seek to
replace or avoid them have been proposed and investigated by the present author
in some of her earlier papers. Because multiple kinds of solution are of
interest to the contamination problem, granular generalizations of RIFs are
proposed, and investigated. Interesting representation results are proved and a
core algebraic strategy for generalizing Skowron-Polkowski style of rough
mereology (though for a very different purpose) is formulated. A number of
examples have been added to illustrate key parts of the proposal in higher
order variants of granular operator spaces. Further algorithms grounded in
mereological nearness, suited for decision-making in human-machine interaction
contexts, are proposed by the present author. Applications of granular
\textsf{RIF}s to partial/soft solutions of the inverse problem are also
invented in this paper.
