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way that a minimal sufficient set of functionalities for
professional work are guaranteed in case of a platform
migration. For GeoTeX, three levels, 0, 1 and 2, were
defined. 0 is the lowest interface level with essentially
no tools other than those provided by the operat-
ing system; 1 is the alphanumeric interface level and
makes no other assumption than the ANSI standard;
2 is the graphic interface level, most likely to degrade
after a migration.
Realistic goal: that the system and the adjustment
program be able to run at least at the interface level
1 (see Section 5.2 and Figure 2) in the three de facto
standard operating systems in scientific computing,
DOS, UNIX and VMS.
2.4 Drawbacks of general approaches
A known effect of design principles similar to those
of the preceeding sections is the proliferation of soft-
ware shells which slow down the execution of some
processes. Since that increase in computing time is
approximately linear, it can be absorbed by faster
hardware. In other words, the extra time budget of-
fered by the new hardware can —and should— be
partly invested in more complex software architec-
tures. (Note that time and space algorithmic com-
plexity remain a problem.)
The real danger of a general approach is that ei-
ther a number of useful tailored functionalities may
be lost or that irrelevant details for a particular sub-
set of applications may burden the operation of the
software.
In this respect, the following design concept must
be fully realized in the implementation.
Design concept: generalization of concepts must
be rigorous enough to make developers write tai-
lored modules —always of an informative auxiliary
nature— in rare ocassions only; privileged users like
project or department managers must have the pos-
sibility to customize the software by setting defaults
and abling/disabling options.
3 ON THE FORMAL STRUCTURE OF
OBSERVABLES
The idea of defining the formal structure of observ-
ables and other elements participating in an adjust-
ment dates back to the late seventies [15, 17] and it
has been driven by the need for automation, either in
the design of geodetic data bases [17] or in the design
of adjustment systems [7, 14].
Probably, there is more than one approach well
suited for the management and reduction of pho-
togrammetric and geodetic data. Even for a given
658
data structure approach, there might be different
complexity levels; for instance, the general data base
might contain information irrelevant to an adjust-
ment.
For the sake of simplicity and because the GeoTeX
data base is not completely defined a simple adjust-
ment oriented point of view will be adopted. Then,
the main data types are: observables, parameters,
constraints and sensors (instruments may be called
as well). The sometimes used term formal structure
of observables, is nothing else than a rough expres-
sion for the definition of abstract data types. The
data type observable is defined in GeoTeX as
<Pn...pi><8...8;><0a1...0 >
(1)
€ 01.5017 € C1... Cn 2
where pi...p; are the identifiers of the parameters
involved, s1...5; the identifiers of the instruments,
a; ... a auxiliary information (meteorological, etc.),
01 ...0; the actual observed amounts, and c; ... Cm
some representation of the covariance matrix.
The above definition is polymorphic in the sense
that a particular observable data type is a particular
case of (1). A photogrammetric observation data type
would be defined as
< P1P2P3 > < 81 > < 0102 > <C...>, (2)
where p; stands for an image orientation parameter,
po for a point, ps for a selfcalibration parameter, s;
for a metric camera sensor, 0102 for the image coordi-
nates, and c; ... for the statistical information. Last,
a photogrammetric observation, i.e. an instance of
the former abstract data type could be
8623 132451 9001 2345.7 —92356.6 7.5. (3)
A control point observable data type would look
like
«7 «0100903. 2 €& C12: (4)
Analogously, the abstract data type parameter is
defined in GeoTeX as
«2| 2«41...0. 2 « 0$...00» «€... 2, (5)
where the meaning of p1, à1 . . - Gk, 01 - . O1, €] - . . Cm, iS
clear.
Constraint and sensor data type definitions follow
the same philosophy.
4 DATA STANDARDS, ABSTRACT
DATA TYPES AND DISCRETE
NETWORK MODELS
It is necessary to differentiate three concepts used
throughout the paper: data [transfer] standards, net-
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