The FCO-IM method contains the following set of structural principles which are additional to the basic principles of NIAM, such as natural language, concrete examples, user interviews and prescription based information modelling.
The purpose of information analysis is not to model the structure of the Universe of Discourse (UOD) itself but to model the structure of the communication about the UOD by the users. The product of an information analysis is an Information Grammar (IG), which formally describes on a type level the structure of the relevant fact stating sentences in the user communication about the UOD.
100% Conceptualisation principle
We'll break this down to the 100% principle and the Conceptualisation principle.
Redundancy free modelling principle
An IG models the user communication about the UOD in a redundancy free way. Each aspect of the communication about the UOD may appear only once in an IG. These principles imply that an information grammar (IG) should model the structure of the fact stating user sentence types also (at least for elementary facts) and in a redundancy free way. The main aspect of our work is an attempt to incorporate these principles in NIAM in a consistent way, because we felt strongly that in N-ary nested NIAM (Nijssen & Halpin, 1989) these principles had not been carried through completely. The way in which we did this in our previous paper was guided by three more principles.
All non-lexical object types are nominalizations of fact types. This principle solves the most serious violation of the communication principle. It ensures essentially that all non-lexical object types are populatable constructs, unifying fact types and non-lexical object types (including subtypes) into a single concept.
Elementary user sentences can be regenerated from the IG plus its label population (LP) by substituting either object type expressions (OTE's) or labels in the roles of fact type expressions (FTE's).
We imposed this principle on ourselves in order to enable the user to verify the correctness of the modelled declarative sentences (i.e. fact expressions of elementary facts). The separate treatment of OTE's apart from FTE's is a direct consequence of the redundancy free modelling principle (P3).
A Generic Meta Grammar (GenMG) is used which can contain IGs in various data models (NIAM, Relational, ...) as its population.
We adopted this principle for theoretical, practical and didactical reasons; theoretical: NIAM and the Relational Model use different terminologies, yet have a lot in common; practical: improvement of CASE-tool architecture; didactically: teaching information systems methodologies in a generic way. In our previous paper we showed how a relational representation of the GenMG allows us to generate a relational schema from a CO-NIAM IG via ordinary updates on the GenMG population. This is accomplished by the Group, Lexicalize and Reduce (GLR) algorithm.