Integrating DROOLS and R software for
intelligent map system

Jan Ruzicka
Institute of Geoinformatics

VSB – TU of Ostrava
jan.ruzicka vsb.cz

Keywords: expert system, map sheet evaluation, DROOLS, R software, ontology

Abstract

The paper describes intelligent map system that allows to check errors in map sheets or to
help with a map sheet creation. The system is based on expert system DROOLS, ontology
created in Protége and statistical software R. Prototype of the system should evaluate that this
kind of integration is possible, so the system is not full of rules. The prototype is filled with
twenty rules written in DRL language and with more than thirty items from the ontology. The
paper should show how all of these components can be integrated together to allow such kind
of a map sheet evaluation. The system is now used for selection of the best method for data
classification. The selection is suggested by DROOLS system that uses R software to perform
statistical tests of normality and uniformity.

Introduction

The world of cartography is changing, we can see it in any map that is available on the
web these days. Any internet user can create own map without any basic knowledge about
cartography. There are tools for a map creation available free of charge and geodata available
free of charge as well. When the tool for a map creation keeps the process of a map creation
under its supervision, the resulting map is usually correct in a term of cartography rules.
When the tool gives a lot of options how to create the map, the map is usually full of mistakes.
This is described in [1]: "Process of making map is core of the whole cartography, but not
only specialists are making maps nowadays. In last years, this process not involved only the
cartographers, but also the common users. Production of map with using adequate software
is a simple process now, which is used by non-cartographic users. These users do not know
basic cartographic rules for making maps and they make maps intuitively. This situation needs
the implementation of principles of cartography directly into the map production systems in
pursuit of correct and effective maps producing. Instead of the final map is also important
the explanation and the proposing of several possible solutions. According to progress in
the artificial intelligence, the knowledge-base systems can be applied for this problem. These
systems can partly substitute a role of the expert in this process." We have decided to research
possibilities how to create intelligent map system, that can help in a process of a map creation.
Several tools has been inspected and tested for purposes of the system development. We
have discovered that such system can not be simply created with one tool, but that several
independent systems should be integrated together. This article describes integration of
expert system and statistical system.

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Ruzicka J.: Integrating DROOLS and R software for intelligent map system

Aim of the system

Aim of the system is to help with a map creation for users that are not familiar with carto-
graphic rules. The system can help in two ways:

• answer a question in a process of a map sheet creation,

• check a created map sheet for mistakes.

When the user creates a map there are always steps where he/she must do a decision. For
example which size of a font to use for a title of the map or which classification method to
use for creating classes breaks. The user just simply (or sometimes not so simply) answers to
system questions and obtains recommendations how to finish the step of the map creation.
The answers can be filled in a simple graphical user environment with items such as text field
or check box. Several answers can be derived from the data used by user for the map creation.
A similar approach has been used in the Descartes project [2] and we just adopted it to our
project.

An another way, not yet researched in deep in any founded article, is based on check existing
(created) map for mistakes. In this approach the system obtains the map from the user and
analyses its content. When it is needed, the system can ask the user for original data. The
map is checked according to cartographic rules. This approach is mentioned in [3], but the
system mentioned in the paper was not tested and not even developed. A result of the check
of the map for mistakes can be of three types:

• a list of mistakes and suggestions how to avoid them,

• a map without mistakes based on the original map,

• a map without mistakes based on the original data and original map.

The simplest way is to provide the user with a list of mistakes and some suggestions how to
avoid them. We can generally declare that system described in this article works according
to this simplest way. The more difficult is to repair the map. To be able to repair the map
there must be meet several conditions:

• the map must be available in the form of file, that uses structures that can help with
simple map repair (e.g. Scalable Vector Graphics format),

• the mistakes must be from the selected types, not all mistakes can be automatically
repaired,

• the original data must be available.

The aim of the system is not to create it so flexible that it is able to find any mistake in
a map, but it should be able to find several most horrible mistakes to help with a map
quality improvement. For example to avoid creation of maps such as on the following figure
(Figure 1).

Pilot project focus

The pilot project is focused only on selected part of cartography techniques namely Choropleth
maps and Cartograms. It has been tested on Atlas of Fire Protection in the Czech Republic

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Ruzicka J.: Integrating DROOLS and R software for intelligent map system

Figure 1: A map with several mistakes

(Ministry of Interior).

The atlas allows to create a choropleth map or a cartogram based on statistical database of
events that required fire brigade action. The atlas allows to specify following conditions:

• year from/to of events,

• type of events (e.g. fire where were injured fireman),

• statistical method for generating class intervals (Jenks, Equal interval, etc.),

• number of classes,

• type of frequency (square km, population),

• start colour, end colour for classes visualization.

The user must specify these conditions. Selection of the statistical method is in the pilot
project now based on the intelligent map system.

The resulting map can be as on the following figure (Figure 2).

System architecture

The system is based on integration of several items listed on the following figure (Figure 3).

The process of answering to the question which classification method to use is covered by
following steps:

• Client (Any SOAP/REST capable – in our pilot project the client is the Atlas) sends
data for classification to service.

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Figure 2: Choropleth map from the Atlas

Figure 3: System architecture

• The service reads an ontology (available in OWL format) and creates objects that will
be placed in a session of an expert system based on DROOLS.

• When is created an instance of a class named StatisticalValuesGeo, the data from the
client are stored into the instance.

• After the data are stored in the instance the instance creates R software instance and
runs tests of the data in the R software instance.

• The service creates the session of the expert system and fires all rules on the session.

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• Results of the all rules run is stored in the InfoContainer class.

• The service reads results from the InfoContainer class and returns response containing
the results to the client.

Ontology

The used ontology is created in Protége software. The ontology is created with regard to limits
of export to Java classes. The export is done via Protége-OWL-API that has several limits
when exporting ontology. So the ontology is just a simple hierarchy with super-classes and
sub-classes. The classes have defined attributes with a data type definition and a cardinality
relationship between class and attribute.

Class StatisticalValuesGeo

The class StatisticalValuesGeo extends class StatisticalValues, that is defined in the ontology.
The extension is based on reaction to the process when data used for a map are stored within
this class. In that moment is tested their statistical distribution.

The distribution is tested only for three possible models:

• Normal distribution.

• Uniform distribution.

• Other distribution.

The test of distributions is done in software R via tool rJava (JRI – http://rosuda.org/rJava/).
The tool rJava is a Java native interface to R software.

Normal distribution

The normal distribution is tested with Shapiro test (module shapiro.test). When the value of
resulting W is more than 0.95 and value of resulting pvalue is more than 0.05 then the data
are identified as they have normal distribution. See the following code for details.
private static boolean testNormality(Rengine re) {

long e=re.rniParse("shapiro.test(p)", 1);
long r=re.rniEval(e, 0);

REXP x=new REXP(re, r);
RVector rv =x.asVector();

x = rv.at(0);
double W = x.asDouble();
x = rv.at(1);
double pvalue = x.asDouble();

if (W > 0.95 && pvalue > 0.05) {
return true;

} else {
return false;

}
}

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http://rosuda.org/rJava/


Ruzicka J.: Integrating DROOLS and R software for intelligent map system

Uniform distribution

The uniform distribution is tested with Kolmogorov – Smirnov test (module ks.test). For the
purposes of the test is created uniform distribution based on minimum and maximum from the
tested data distribution When the value of resulting D is less than 0.1 and value of resulting
pvalue is more than 0.05 then the data are identified as they have uniform distribution. See
the following code for details.
private static boolean testUniformity(Rengine re) {

REXP x = re.eval("y=c(min(p):max(p))");
long e=re.rniParse("ks.test(y, p)", 1);
long r=re.rniEval(e, 0);
x=new REXP(re, r);

RVector rv =x.asVector();
x = rv.at(0);
double D = x.asDouble();
x = rv.at(1);
double pvalue = x.asDouble();

if (D < 0.1 && pvalue > 0.05) {
return true;

} else {
return false;

}
}

DROOLS

The DROOLS system is used for testing defined cartographic rules. The rules are written in
DRL language. For example of decision which classification method to use are used following
three rules.
rule "NormalDistribution"
when

N_StatisticalValuesGeo ( distribution == "Normal" )
then

InfoContainer.method = "EqualInterval";
end

rule "UniformDistribution"
when

N_StatisticalValuesGeo ( distribution == "Uniform" )
then

InfoContainer.method = "Quantile";
end

rule "OtherDistribution"
when

N_StatisticalValuesGeo ( distribution == "Other" ) or \
N_StatisticalValuesGeo ( distribution == "Unknown" )

then
InfoContainer.method = "Natural";

end

There are also rules to test when is used another classification method that is recommended
by the system according to detected distribution.
rule "NormalDistributionClassificationScheme"

when
not EqualIntervalScheme() and N_StatisticalValuesGeo ( distribution == "Normal" )

then

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Ruzicka J.: Integrating DROOLS and R software for intelligent map system

InfoContainer.addMessage("[10] When is distribution of the data normal, the Equal Interval \
classification scheme should be used");

end

rule "UniformDistributionClassificationScheme"
when

not QuantileScheme() and N_StatisticalValuesGeo ( distribution == "Uniform" )
then

InfoContainer.addMessage("[11] When is distribution of the data uniform, the Quantile \
classification scheme should be used");

end

rule "OtherDistributionClassificationScheme"
when

not JenksScheme() and N_StatisticalValuesGeo ( distribution == "Other" )
then

InfoContainer.addMessage("[12] When the distribution of the data is not normal or linear, \
the Jenks classification scheme should be used");

end

Problems

There were several problems with integration of R software with DROOLS system.

Integration to JBOSS

When is system run in a single user environment, then there is not any problem. When we
decided to place the prototype into JBOSS application server (that is our primary application
server for running DROOLS system in multiple user environment), then the R engine instance
does not end in the correct way. There was not still the time to fixed this problem so we
use simple workaround. There is used solution based on stateless CGI interface that is called
from JBOSS engine.

Where to implement call of R

After several test it is not still clear where to place code for running R software. More
general solution could be based on separated class, that will work as a proxy for classes that
would like to use capabilities of R software. This solution should be used only when the
problem with integration to JBOSS will be fixed. At a moment is all code written in the class
StatisticalValuesGeo.

Server with older version of R

The server dedicated for our test purposes uses old version of R software and it is difficult to
move to newer version. So we had to handle two problems:

• The older version does not support function assign. We fixed this with simple convert
array to vector.

• The R engine must be run with –vanilla parameter. This was quite hard to find out,
because nobody mentioned this problem on any discussion forum.

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Conclusion

As a part of our research we did integration of DROOLS with R software. Our findings are
simple, but possibly valuable:

• The integration is possible, but at the moment not with a good performance (CGI
workaround)

• The solution based on integration of DROOLS and R software allows in the future to
use another functions from R.

• R engine can be replaced with another tool (the solution is not directly dependent on
the R engine).

References

1. Brus J., Dobesova Z., Kanok, J.: Utilization of Expert Systems in Thematic Cartog-
raphy. In: Badr Y., Caballe S., Xhafa F., Abraham A., Gros B. (Ed.), INCOS ’09
Proceedings of the 2009 International Conference on Intelligent Networking and Collab-
orative Systems. pp. 285–289. IEEE Computer Society Washington, DC USA. ISBN
978-0-7695-3858-7 (2009)

2. Andrienko, G., Andrienko, N.: Knowledge engineering for automated map design in
DESCARTES. In: Advances in Geographic Information Systems, ed. by Medeiros,
C.B., 7th International Symposium ACM GIS’99, Kansas City, November 1999 (ACM
Press, New York 1999) pp. 66-72 (1999)

3. Růžička, J.: Pomohou webové služby odstranit noční můru kartografů?. 16. kar-
tografická konference (Mapa v informační společnosti)., Univerzita Obrany, 2005, s.
1-10. ISBN 80-72-310-151 (2005)

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