The main goal of the project
is to design and develop a prototype of a multi-agent tool for intelligent
data analysis and implicit knowledge management of data bases, with special
focus on environmental data bases. It is remarkable the high quantity of
information and knowledge patterns implicit in large data bases coming
from the monitoring of any system or dynamical environmental process, for
instance, historical data collected about meteorological phenomena in a
certain area, about the performance of a wastewater treatment plant, about
characterizing environmental emergencies (toxic substances wasting, inflammable
gas expansion), about geomorphologycal description of seismic activity,
etc.
All this information and
knowledge is very important for prediction tasks, control, supervision
and minimization of environmental impact either in Nature and Human beings
themselves. The tool that is proposed here will be composed by several
statistical data analysis methods (oneway and twoway descriptive statistics,
missing data analysis, clustering, relations between variables) as well
as several machine learning techniques, coming from Artificial Intelligence
(clustering, case based reasoning techniques, reinforcement learning, dynamical
analysis).
As main differences from
the existing commercial systems, the more relevant aspects of this proposal
are: the interaction of the developed methods, the development of mixed
techniques that can cooperate among them to extract the knowledge contained
in data, the existence of dynamical data analysis, and the existence of
a recommender agent which will suggest the best method to be used depending
on the target domain and on the goals especified by users.
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