INTELLIGENT EXPANSION OF THE GEOINFORMATION SYSTEM
A. Ryumkin1, A. Yankovskaya2
1 Tomsk
State University of Architecture and Building,
2, Solyanaya Square, 634003, Tomsk, Russia, yank@tisi.tomsk.su, yank@tsuab.ru
2 Tomsk State University, 36, Lenin pr., 634050, Tomsk, Russia, rai@sibgeoi.tomsk.ru
The intelligent expansion tradition vector GIS with the use of intelligent recognizing system is proposed. Integration of two systems is put in a basis of intelligent expansion GIS –
typical vector GIS and the intelligent recognizing system based on software tool IMSLOG
with the purpose of union of functionalities of both. Original test methods of pattern recognition are used.
Introduction
There is a fairly wide list of commercial software products in the field of geoinformation
system (GIS) in the world scale as well as markets. The basic world leaders in the given
sphere of the software are products of the
American firms: ESRI, MapInfo, ERDAS, Autodesk, Intergraph [1]. Prominent features of
this software are: 1) presence of the advanced
means of representation of the graphic information both vector formats and raster; 2) opportunity of use of databases such widespread
DBMS as Oracle, MS SQL Server, dBase,
FoxPro and a number of others. The majority of
systems of the given class give users support of
a big number of the various geographical projections, advanced means of the analysis of the
graphic information represented in various formats, advanced means of graphic search.
However the enumerated software products do
not contain components using knowledge bases
for the solution of wide a number of problems,
and are not equipped with intelligent means,
which does not allow to solve practically important problems for which heterogeneity of the
processable information, an fuzzybility and
great volume of the data and knowledge are essential. The problems of decision-making at
planning, development of territories of cities or
regions, estimations of the real estate, an ecological condition, expediency of investments
[2] are typical applications. Research on application of methods of an artificial intellect in
GIS are at the initial stage. In widespread raster
GIS ERDAS IMAGINE, intended for processing images (including for GIS), subsystem
IMAGINE Expert Classifier using production
rules of knowledge representation at the solution of problems of classification has appeared
recently. In some vector GIS there are also elements of AI, but all this has trial character.
Problem of investigation developed by us is
construction of the geoinformation system possessing possibilities of representation, processing and application of knowledge on the
basis of original methods of an artificial intellect.
Bases of intelligent expansion of
geoinformation system
Integration of two systems is put in a basis of
intelligent expansion GIS – typical vector GIS
such as ArcVier [1] or the GrafIn [3] and the
intelligent recognizing system based on software tool IMSLOG [4] with the purpose of union of functionalities of both: effective means
of extraction, systematization, representation
and data processing about the territories realized in GIS, and intelligent means of the data
and knowledge analysis [5]; revealings of a various regularities in the data and knowledge; decision-making on the basis of a combination of
various schemes (mechanisms) of a inference
logic-combinatory (l-c) [5], logic-probabilistic
(l-p) [5,6], logic-combinatorial-probabilistic (lc-p) [7] and voting procedures on a set of solv-
ing rules constructed on a set of logic tests (unconditional and mixed, ones which are a compromise between unconditional and conditional
components) at each scheme of a inference as
well as various, oriented on users of different
qualification, graphic (including cognitive)
means of visualization of information structures, regularities, decision-making and substantiation of the decision-making results [8]
realized in intelligent system.
The structure traditional GIS usually includes
the advanced graphic editor and the block of
management of the attribute data, allowing for
the description of a subject area to use a composition of graphic and attribute components. In
vector GIS for this purpose a set of graphic
primitives (a point, a line, polygon) is used.
Each them is characterized by a set of the attribute data. In aggregate they form the digital
terrain models (DTM) equivalent to usual not
spatial databases. In GIS the modules of the
spatial analysis using geometrical operations
above graphic objects, as well as usual for
DBMS operations above tables of the data are
realized. Compositions of these operations, mutual transitions between them give the user
convenient means of work with (DTM) in the
dialogue. It is easy to see that similar models of
the data are also convenient for the application
to this problem area of methods of AI. One of
the major in construction GIS is the stage of
formation DTM on materials of aerial photograph, space shooting, or on available cartographical materials. Here, and also in many
other problems GIS methods of pattern recognition and images analysis [9] are used. Formalizing typical for GIS spatial relations and conditions it is possible to receive typical statement
of a problem.
It is supposed to carry out realization and preliminary debugging of the intelligent block of
system on the basis of methods of test patterns
recognition.
Applied methods
The test approach to pattern recognition assumes performance of the following stages.
1. Adaptive code conversion of characteristic
features of a different type (quantitative, nominal, serial) with the purpose of the maximal
partition of classes (patterns) and reduction a
kind used in a matrix methods of data and
knowledge representation [5,6].
2. The analysis of a database and knowledge on
consistency (check of paired crossing of descriptions of objects from different patterns
(classes on each mechanism of classification)).
3. Revealing of knowledge representativenes in
two ways: logic-combinatory with deep optimizing transformations and statistical methods
on the basis of so-called divergence of Koulbac
information; an estimation (on a basis of
Koulbac divergence) of additional volume of
training knowledge with a purpose of obtaining
reliable conclusions.
4. Construction of irredundant implication matrix U' (with simultaneous revealing constant,
steady, noninformative, obligatory, alternative
and dependent features and calculation of
weight coefficients of all features), assigning
either necessary and sufficient conditions or
sufficient conditions of distingrushability of
any pair the objects belonging to different patterns (to classes at each mechanism of classification) [5].
5. The finding of all shortest (all or a part irredundant) columns coverings of matrix U', to
each of which corresponds minimal (irredundant) distinguishing subset of features (minimal (irredundant) test), assigning necessary and
sufficient (sufficient) conditions of distingrushability of any pair of objects from different patterns. Revealing of nonexistent and pseudoobligatory (at construction only of a part irredundant column coverings of matrix U') features. Construction of all minimal (all or a part
irredundant) the unconditional and mixed tests
[10].
6. Construction of a set of the solving rules taking into account all found regularities and realizing a set of independent ways of recognition
(schemes of a logic inference) of the same ob-
ject under investigation (OUI). The number of
recognition ways is equal to the number of tests
used for recognition [5].
7. Recognition of the OUI by one of approaches
(at the l-c approach – on the basis of similarity
coefficients and taking into account of an admissible error of decision-making assigned by
the user [11]; at l-p approach - on the basis of a
partial implication at a partial ortogonalization
of some DNF of Boolean functions describing a
pattern in space of features included in the minimal (irredundant) test [6]; at l-c-p approach –
on the basis of similarity coefficients taking
into account of values probability of some features of the OUI) [6].
8. Acceptance of the final decision on results of
voting on a set of ways of recognition (tests)
and approaches.
9. Application of various cognitive means of
acceptance and a substantiation of decisions
and graphic means of visualization of information structures and the revealed regularities
[8].
We list the basic approaches to construction of
tests for 2-, 3-and k-values features: 1) with
construction irredundant implication matrix U';
2) with partial construction of the matrix U'; 3)
without construction of the matrix U'.
A particular approach is applied. This depends
on the dimension of knowledge (the number of
characteristic, classification features, the number of rows of matrix Q), of knowledge, and its
representativeness. In the first approach, one of
the following algorithms can be executed: a)
the search for all shortest column coverings
with simultaneous detection of the regularities;
b) the search of irredundant tests with simultaneous detection of obligatory features, with calculation of the weight coefficients feature and
with the use of a step-cyclic algorithm; c) the
search of the minimum and irredundant unconditional tests with the use of genetic algorithms.
At the second and third approaches the algorithms similar to algorithms b,c from the first
approach are used. Addition of columns at construction of minimal and irredundant descriptions matrices is carried out to nonexcluded
subset of columns, corresponded to the core of
diagnostic tests, and at removal columns the
ones corresponded to the core are not removed.
Algorithms of construction unconditional and
mixed minimal and irredundant tests at the first
approach in case of realization of algorithm a)
include the following steps: 1) calculation of a
distinguishing vector-function for the next pair
a class-class (an object-class, object-object)
from different classes at the fixed classification
mechanism (a pattern-pattern, an object-pattern,
object-object from different patterns for nonpartitioned matrix U'); 2) calculation of weight
features coefficients; 3) revealing of obligatory
features and, at presence of which, their inclusion in a core; 4) addition of the next value of a
distinguishing vector function to the current
matrix U; consecutive formation of the vector
used for revealing of constant features and vectors for each patterns (a class on each classification mechanism) for revealing steady features
and removing in the current matrix U of covering rows; 5) construction of matrix U' with
simultaneous revealing a part of regularities; 6)
Construction of a set of all possible shortest
(optimum) column coverings of a matrix U'
with the use of features weight coefficients; 7)
construction of the minimal (optimum) unconditional diagnostic test on each from the shortest (optimum) column coverings; 8) construction of the mixed test on the base of unconditional test with inclusion of obligatory features
in a unconditional component of the test; 9)
calculation of weights of diagnostic tests.
Let's note that features are considered obligatory if in matrix U there are rows containing only
one unit in columns corresponding to these features. Features are considered to be alternative
if corresponding to them columns matrix U are
equal. Features to which zero columns of matrix U correspond are not informative as far as
they do not distinguish any pair of objects from
different patterns. If the i-th the column of matrix U is covered by j-th (not equal to the i-th)
column corresponding i-th column the feature
distinguishes only some pairs objects from a set
of pairs, distinguished by j-th feature, and it is
considered a dependent one.
The features, which have not been included in
all shortest (irredundant) coverings of the matrix U', are considered nonessential at decisionmaking on the basis of minimal (irredundant)
tests and are not used for the description of OUI
and decision-making.
Search of all shortest column coverings of matrix U' is based on construction on matrix U' the
hierarchical system of submatrices being a tree
of search, and are reduced to selection of all
nonrecurrent shortest ways in a tree of search.
In a basis of algorithms of construction of a tree
of the mixed test [12] at the first approach (of a
partial tree of the mixed test on presentation of
the object description at the second approach)
on the unconditional test procedure of partition
of rows of a descriptions matrix on a set of reactions on values of obligatory and pseudoobligatory features lies.
Partition coefficient C is used for forming of a
conditional component of the mixed test. The
partition coefficient reflect a degree of nonintersection of patterns subsets corresponding to
different feature values. The feature with maximum coefficient C is chosen on every step of
construction of a tree of the mixed test. The
calculation procedure of a number of voices
given for the test is used for a final solution.
Conclusion
Intelligent expansion of a geoinformation system will allow to develop the existing traditional possibilities of geoinformation system directed at visualization of territory data, the spatial analysis with the use of solving rules allowing to carry out classification of spatial situations and to take administrative solution for the
specialized applications.
This work was supported by the Russian Foundation for Basic Research, project nos. 01-0100772 and 01-01-01050).
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