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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