Information Technology Reference
In-Depth Information
The above semantic information associated with the analysed economic data pre-
sented in the form of financial ratios allows a detailed identification of the type of
situation (whether it is pathological or is a phenomenon expected and accepted by the
company management with regard to the considered investment) prevailing within the
company.
It must be borne in mind that changes taking place inside companies are brought
about by various types of situations, phenomena and determinants. These situations
may be either external or internal. This is why defining the right patterns applied to
UBMSS systems which will be taking strategic and business decisions is very diffi-
cult, as it requires analysing a whole range of various factors that can have a signifi-
cant impact on the decision-making process. It is because of this fact that the UBMSS
systems presented in this chapter for supporting the right decision whether to make
(or forego) a given investment greatly help choose the best decision and determine
whether the investment under consideration is acceptable or not; and if the decision is
acceptable, then whether the acceptance is unconditional, or whether there is a certain
danger (risk) inherent in implementing it (this situation is illustrated by the minimum
permissible values of financial ratios selected for analysing).
UBMSS systems are of great help in understanding the analysed economic, finan-
cial and strategic situation with regard to the analysed company, investment and strat-
egy. So they are systems which perform a very important type of analysis - a cogni-
tive, interpretational, reasoning and forecasting analysis based on mechanisms of the
linguistic and meaning-based description of data.
6 Conclusions
Artificial intelligence techniques used to analyse complex data and build a new gen-
eration of intelligent information systems are discussed assuming a clear dedication to
a specific field of application. Due to the legibility and the exceptional character of
operations described here when applied to images (which are complex data structures
particularly easily penetrated by the human mind), frequent references to image-type
data were used in all examples. However, it has also been shown that numerical data
can be analysed and its cognitive analysis can be conducted no less effectively if
linguistic data recording formalisms are used to do this.
The cognitive information systems considered here, using cognitive data analysis,
are diverse, and their diversification results from the broad range of opportunities for
applying particular techniques. Data stored in information systems is today subjected
to broad analyses to improve this data (e.g. image) quality, to analyse the meaning of
this data, to recognise it, understanding it and reason based on the information sets
available.
A look at the newest trends in the development of intelligent information systems
shows that the pre-processing, analysis and classification (recognition) of the data in
question is no longer sufficient. This is why it is proposed to orient these systems
towards the automatic understanding of the meaning of the analysed and processed
data, e.g. of the semantics of the studied data sets. The human mind has incomparably
greater perception skills than even the best programmed computer, which allows the
mind to reach the meanings characteristic for the observed objects or analysed data
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