Information collection pertains to different production lines (control
network densification, terrain relief modelling, image transformation,
mapping, and other), and their interrelationships.
— Concepts pertaining to the uses of information products can be clas-
sified coarsely according to administration levels (single versus
multiple) and according to uses (single versus multipurpose; table 1).
USE LEVEL
(or purpose) Single Multiple
Single X X
(Project program)
Multi X X Integration in
(projects, programs) users’ domain
Generalísation
Table 1: Administration levels versus system uses.
Models and modelling can be approached from different perspectives. Most
significant seem to be the domains of data base, model representation,
system functions, and the system hierarchy.
- Models concern the semantic and metric domains of information and data,
and compositions of both.
— Model representation can be algorithmic (algebraic, Boolean,
statistical), numerical, graphical, pictorial, textual and mixed.
- Models for system functions can be assigned to the phases of system
evolution, i.e., design and development, operation and support, and the
uses of information. Operation and uses need to be further
differentiated.
- Models pertain to different levels of a system hierarchy, i.e. the
broad system, basic system (under consideration), sub-systems, sub-sub-
systems, and components.
Àn important issue concerning the assessment of concepts and models is
their feasibility. Feasibility refers to the features or phenomena being
conceived and modelled, and to the methods of assessment.
- The features involved are technical (performance, reliability, etc.)
economic (cost, production rate, etc.) and social (skills, convenience,
etc.).
- The methods for systems assessment can be analytical (theoretical)
semi-analytical, or experimental.
3 Data base structures
À basic requirement for an integrated system is the ability to merge in-
formation and data from different sources. For structuring a data base,
it is necessary to identify first the influencing factors, such as the
overall system context, main process stages, destination, domains, and
specific context of the data base. These factors provide the main entries
for hierarchical classification schemes.
Also significant are interrelationships between data base structures and
corresponding procedures (including algorithms), and conversions of
structures between sucessive process stages.
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