=Paper=
{{Paper
|id=Vol-3241/paper18
|storemode=property
|title=Algorithm for the Information Space Forming and the Evaluation of Input Objects Search Efficiency
|pdfUrl=https://ceur-ws.org/Vol-3241/paper18.pdf
|volume=Vol-3241
|authors=Vadym Mukhin,Valerii Zavgorodnii,Yaroslav Kornaga,Lesia Baranovska
|dblpUrl=https://dblp.org/rec/conf/its2/MukhinZKB21
}}
==Algorithm for the Information Space Forming and the Evaluation of Input Objects Search Efficiency==
https://ceur-ws.org/Vol-3241/paper18.pdf
Algorithm for the Information Space Forming and the Evaluation
of Input Objects Search Efficiency
Vadym Mukhin 1, Valerii Zavgorodnii 1, Yaroslav Kornaga 1, and Lesia Baranovska 1
1
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Peremohy Avenue, 37,
Kyiv, 03056, Ukraine\
Abstract
Concept, algorithm and scheme of information space formation is considered in this paper.
This algorithm involves the use of an information system, which is actually a software basis
for maintaining the information space. Identification of the input object in the information
space allows us to identify it univocally according to the relevant features. For this, an
identification method based on consecutive analysis of the object's characteristics is used.
Classification of the information objects in view to add them to the information space on basis
of multidimensional discriminant analysis is considered. Efficiency analysis of the input
objects search in information space is perform.
Keywords 1
information space, information object, parameters, discriminant analysis, object identification,
search method
1. Introduction
Unified information space can be considered as the information model of a complex subject area. It
includes the information objects, the links between them, the space environment and the processes for
the creation the unified information space [1–4]. Unified information space is form as result of
processing information about an object, received from various sources [5–9].
The contradiction arises between the heterogeneous nature of sensors for collecting features of
objects and the requirement for unified data presentation [10, 11]. In this case, the same object, the
parameters of which obtained from different sensors, should be identified uniquely anyway [12, 13].
An information object is a formal description of some object by its main parameters. It describes as
a tuple of parameters of the object, and all values of parameters are determined by characteristics of
real object. The information about objects in the unified information space changes in dynamic way [1,
14, 15]. The design of the unified information space allows us to provide a unified description of
information objects for all users, and the all users will receive the same information object in the same
way. This feature is the key factor for the unified information space [2, 16, 17].
The unified information space allows us to give the access to the common information without the
limits for place and time. The computer system is form as the basis for unified information space and it
performs the following main functions: transformation of information about objects and formation of
unified in-formation space; to provide users with information about objects.
The creation of an information space is intended to provide access to some common information
without the space and time limits. The basis of the information space is a set of computer systems, local
networks, open networks (Internet), software (operating system, applications, databases, mail services,
etc.). Also, the creation the information space is based on the mechanisms that combine the different
information systems to each other.
XXI International Scientific and Practical Conference "Information Technologies and Security" (ITS-2021), December 9, 2021, Kyiv, Ukraine
EMAIL: v.mukhin@kpi.ua (V. Mukhin); v.zavhorodnii@kpi.ua (V. Zavgorodnii); ya.kornaga@kpi.ua (Y. Kornaga);
lesia@baranovsky.org (L. Baranovska)
ORCID: 0000-0002-1206-9131 (V. Mukhin); 0000-0002-8347-7183 (V. Zavgorodnii); 0000-0001-9768-2615 (Y. Kornaga);
0000-0003-0024-8180 (L. Baranovska)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
193
The main purpose of the information space is to unambiguously compare each input object to the
respective information object in information space [18–21]. The identification of the input object in the
information space allow us to uniquely identify it by the appropriate parameters. The identification
method, based on a consecutive analysis of the parameters of the input object, can be used for this
purpose [22–24].
Identification of the information object is performed by the external or internal parameters, taking
into account the interaction of the information object in the information space. To do this, each
information object is assigned a set of parameters that, to some extent, characterize the object, i.e. form
the image of the information object. Similarly, an information request to input object is formulated as
brief description [25–27].
Due to this, the procedure of the input object identification is just a simple comparison of its
parameters with a certain image of the query [28–30]. In the case when the parameters of the input
object in the necessary and sufficient way match the parameters of initial object, we may consider that
the input object has been identified successfully.
2. Algorithm for the information space forming
We present an algorithm for the information space design that based on the information system
(Fig. 1):
Figure 1: Algorithm for the information space formation
1. Information system is a distributed system that performs the conversion of information from
various sources and usually it comes in different formats, into a single set of information object’s
parameters, by which users of information space can uniquely identify the input object.
2. The user's entry point into the information system is created. There may be many such entry
points.
194
3. A single hardware space formed for the user regardless of the certain entry point. That is, to
process the information request of any user, the same hardware is used.
4. The software tools are available to the user from anywhere.
5. Each user has an account, which contains the software tools that are often used by him, as well as
his actions, including in relation to specific services.
6. User accounts are stored remotely and user may access them from the different login points.
7. The user enter into the information space via entry point, and there is performed the adjusting of
his environment, i.e. the services that he often used are fixed, and his actions in for the specific services
are analyzed.
8. As a result, the information system is present to the user as a single space, regardless of the current
entry point.
Figure 2 shows the general scheme of the information space formation.
Figure 2: The general scheme of information space formation
The data collection in the information space and its formation is presented in the form of a graph,
which reflects an integrated model of the information space, where the nodes of the graph correspond
to the information objects, and arcs are the relationship between these objects.
Formally, such a model is described as:
𝐼𝑆 𝐼𝑂, 𝑃, 𝑅, 𝐶 , (1)
where 𝐼𝑂 is the set of information objects; 𝑃 is the set of parameters of information objects; 𝑅 is the set
of connections types; 𝐶 is mapping that specifies a certain relationship from set of relationships between
the information objects.
We define the set of connections types as the union of the set of connections between information
objects and the set of connections between the parameters of information objects:
𝑅 𝑅 𝑅 , (2)
where 𝑅 is the set of connections between information objects; 𝑅 is the set of connections between
the parameters of information objects.
In turn, the single information space is a union of information spaces:
195
𝑈𝐼𝑆 𝐼𝑆 , (3)
where 𝑚 is the number of information spaces.
Moreover, each single information space is presented as a graph:
𝑈𝐼𝑆 𝐼𝑆 𝐼𝑂, 𝑃, 𝑅, 𝐶 , 𝑅, 𝐶 , (4)
where 𝐼𝑆 is the set of information spaces; 𝑅 is the set of connections types, which is similar to expression
(2) are the union of a set of connections between information objects of information spaces 𝑅 and the
set of relationships between the parameters of information objects of information spaces 𝑅 , i.e. 𝑅
𝑅 ⋃ 𝑅 ; 𝐶 is mapping that specifies a certain relationship of set of types between set of the information
spaces 𝐼𝑆.
For each input object 𝑂 𝑖 1, 𝑚 , that enters the inputs of the information space and
information object 𝐼𝑂 𝑗 1, 𝑘 , which already are in the information space, the set of parameters 𝑃 ,
is determined. In case when 𝑃 , 𝑃 ⋂ 𝑃 does not completely match the set of parameters 𝑂 and 𝐼𝑂 ,
such an input object 𝑂 is considered new and is added to the information space 𝐼𝑆.
Otherwise in case when 𝑃 , 𝑃 ⋂ 𝑃 matches the set of parameters 𝑂 and 𝐼𝑂 , such an input object
𝑂 is considered identified and is not added to the information space 𝐼𝑆.
Also, the links 𝐶 between the new information object and the existing information objects of the
information space 𝐼𝑆 are added. An important goal of the information space design is to transform the
input information in the certain way when each information object in the information space will be
present unambiguously.
The parameters of information objects in the information space should be defined in unified format
and their number must be the same. The users of the information space should also perceive information
objects unambiguously. Usually, the input data in the information space has heterogeneous nature. The
goal of the internal mechanisms of the computer system is to transform the heterogeneous information
coming in different formats and from different sources into a single set of parameters of information
objects, by which the users of information space can uniquely identify the input object.
3. Classification of information objects in the information space
We use the multidimensional discriminant analysis to classify information objects in order to add
them to the information space. To do this, for each information space we built a discriminant function
in the following form:
𝑑 𝛼 𝛼 𝑝 ⋯ 𝛼 𝑝 , (5)
where 𝑑 is the value of the discriminant function for 𝑗-th information object in ℎ-th information
space; 𝑝 are the parameters 𝑗-th information object that are significant (have a value other than
NONE) for ℎ-th information space, 𝑔 1, 𝑛; 𝛼 is the free coefficient of discriminant function, which
corresponds to ℎ-th information space, ℎ 1, 𝑙; 𝛼 are the coefficients of the discriminant function,
which correspond to the significant parameters of each information space, 𝑔 1, 𝑛.
Then, for each information object 𝐼𝑂 𝑗 1, 𝑘 in each information space 𝐼𝑆 ℎ 1, 𝑙 , we
calculate the value of the discriminant function 𝑑 .
Then, based on a set of values 𝑑 for each information space, we calculate the main statistical
characteristics of the obtained values of discriminant functions: expected value 𝑀 𝑑 and dispersion
𝐷 𝑑 . These characteristics make it possible to determine the affiliation of a new information object
to a particular information space.
For each input object 𝑂 , which should be added as a new information object 𝐼𝑂 , the values of
discriminant functions are calculated 𝑑 and are compared with the obtained statistical characteristics
for each information space. New information object 𝐼𝑂 will be added to the information space in
case when its characteristics meet the following condition:
196
𝑀 𝑑 𝐷 𝑑 𝑑 𝑀 𝑑 𝐷 𝑑 . (6)
The implementation of the above discriminant analysis allow us determine the certain information
space to which the new input information object should correspond.
4. Simulation of identification process for input objects in the information
space
Let us analyze the effectiveness of the input objects search in the information space. We created an
information space of 20,000 information objects for the experiments. The part of the missing parameters
in the information objects (NONE) was set at the level 6%. After that, the information space was
reconstructed by excluding of the duplicate information objects. We performs 100 experiments with
certain probabilities (5, 10, 15, 20, and 25%, respectively), which reflects the fact that the parameter
would not be read by sensors (NULL).
Let us consider the case when every of the 20,000 information objects is described by 7 parameters.
Table 1 shows a fragment of 10 information objects (𝐼𝑂 𝐼𝑂 ) of the information space (𝐼𝑆).
Table 1
A fragment of the is information space of 10 information objects
Parameters
ІО
Р1 Р2 Р3 Р4 Р5 Р6 Р7
IO1 2±0.2 2±0.7 3±0.4 6±0.4 7±0.6 10±0.6 11±0.4
IO2 2±0.2 6±0.7 NONE 6±0.9 9±0.8 9±0.7 NONE
IO3 2±0.3 5±0.4 4±0.1 8±0.2 5±0.9 10±0.3 8±0.5
IO4 5±0.4 6±0.6 NONE 7±0.4 5±0.6 8±0.5 NONE
IO5 1±0.7 6±0.3 5±0.9 7±0.8 5±0.8 8±0.5 10±0.9
IO6 5±0.4 4±0.4 3±0.7 6±0.4 5±0.9 8±0.4 8±0.1
IO7 NONE 4±0.5 7±0.6 6±0.2 9±0.2 6±0.4 8±0.9
IO8 3±0.4 2±0.6 3±0.8 4±0.2 6±0.5 6±0.6 11±0.4
IO9 2±0.3 3±0.4 5±0.6 5±0.5 8±0.6 8±0.2 NONE
IO10 4±0.7 4±0.2 5±0.3 4±0.5 7±0.5 6±0.9 10±0.9
In this case, the interval length for each parameter is 5 units, for example, for parameter P1 [1;6).
The following results obtained:
The sensors read all parameters values for the input object and there was an unambiguous
identification:
New object:
1.6 2.9 4.2 4.7 5.3 8.5 8.4
Search object:
IO 00603 1±0.6 2±0.6 4±0.6 4±0.6 5±0.6 8±0.4 8±0.5
The sensors read all parameters values of the input object and did not identify it, it means that no
information objects corresponding to the following input object was found in the information space:
New object:
4.9 3.3 4.5 7.9 5.2 6.5 11.7
Search object:
Object absent!
In this case, the input object considered as a new information object and it can be added to the
information space.
197
The sensors get the values for incomplete set of parameters of the input object (there are NULL
values), but due to the interaction of information objects with each other in the information space, still
there was a unique identification of the input object in the information space:
New object:
2.8 Null 2.6 5.8 5.9 6.4 8.3
Search object:
IO 01849 2±0.4 4±0.4 2±0.6 5±0.6 5±0.4 6±0.6 8±0.5
IO 04387 2±0.5 2±0.7 2±0.6 5±0.6 5±0.4 6±0.5 8±0.5
IO 05956 2±0.1 3±0.6 2±0.6 5±0.2 5±0.1 6±0.5 8±0.7
ReCreateObject:
2.8 2.2 2.6 5.8 5.9 6.4 8.3
Search object:
IO 04387 2±0.5 2±0.7 2±0.6 5±0.6 5±0.4 6±0.5 8±0.5
The sensors get the values for incomplete set of parameters of the input object but, despite the
interaction of information objects with each other in the information space, the identification of the
input object did not occur:
New object:
2.1 Null 5.3 Null Null 9.3 8.9
Search object:
IO 00391 2±0.9 None 5±0.8 8±0.3 6±0.1 9±0.9 8±0.4
IO 01210 2±0.1 3±0.8 5±0.2 5±0.6 6±0.1 9±0.2 8±0.9
IO 01460 2±0.7 2±0.4 5±0.9 6±0.9 7±0.8 9±0.2 8±0.9
IO 03248 2±0.2 6±0.6 5±0.6 6±0.3 8±0.6 9±0.5 8±0.6
IO 06463 2±0.4 2±0.5 5±0.2 8±0.5 6±0.6 9±0.6 8±0.6
IO 06791 2±0.4 4±0.4 5±0.5 4±0.7 9±0.3 9±0.9 8±0.8
IO 08241 2±0.7 6±0.9 5±0.1 7±0.8 7±0.1 9±0.6 8±0.2
IO 09634 2±0.7 6±0.3 5±0.9 8±0.6 6±0.9 9±0.7 8±0.7
IO 10339 2±0.8 None 5±0.6 4±0.1 7±0.6 9±0.9 8±0.1
IO 10744 2±0.2 6±0.2 5±0.6 4±0.5 8±0.5 9±0.1 8±0.6
IO 10806 2±0.1 4±0.2 5±0.5 8± 0.2 8±0.8 9±0.8 8±0.7
IO 11204 2±0.8 3±0.3 5±0.8 6±0.7 7±0.4 9±0.3 8±0.1
IO 12128 2±0.6 2±0.3 5±0.8 7±0.1 5±0.6 9±0.1 8±0.5
IO 12679 2±0.3 3±0.7 5±0.6 None 9±0.3 9±0.8 8±0.8
IO 13174 2±0.5 4±0.6 5±0.6 5±0.9 5±0.5 9±0.6 8±0.6
IO 13400 2±0.5 None 5±0.7 7±0.7 5±0.4 9±0.6 8±0.8
IO 14805 2±0.2 4±0.9 5±0.6 None None 9±0.7 8±0.9
IO 15221 2±0.6 5±0.1 5±0.1 4±0.5 6±0.2 9±0.1 8±0.5
IO 15884 2±0.4 5±0.9 5±0.5 7±0.3 6±0.3 9±0.8 8±0.2
IO 16132 2±0.5 2±0.1 5±0.2 None 8±0.2 9±0.1 8±0.6
IO 18388 2±0.1 5±0.7 5±0.8 7±0.1 8±0.1 9±0.9 8±0.6
ReCreateObject:
2.1 Null 5.3 Null 8.9 9.3 8.9
Search object:
IO 03248 2±0.2 6±0.6 5±0.6 6±0.3 8±0.6 9±0.5 8±0.6
IO 10744 2±0.2 6±0.2 5±0.6 4±0.5 8±0.5 9±0.1 8±0.6
IO 10806 2±0.1 4±0.2 5±0.5 8±0.2 8±0.8 9±0.8 8±0.7
IO 16132 2±0.5 2±0.1 5±0.2 None 8±0.2 9±0.1 8±0.6
IO 18388 2±0.1 5±0.7 5±0.8 7±0.1 8±0.1 9±0.9 8±0.6
ReCreateObject:
2.1 Null 5.3 6.1 8.9 9.3 8.9
Search object:
IO 03248 2±0.2 6±0.6 5±0.6 6±0.3 8±0.6 9±0.5 8±0.6
ReCreateObject:
2.1 2.5 5.3 6.1 8.9 9.3 8.9
Search object:
198
Object absent!
5. The sensors did not get the value of one or more parameters and after the interaction of the
information objects between themselves in the information space it was found that this object did not
have this parameter (NONE).
New object:
2.7 4.7 4.1 8.3 5.5 Null 7.6
Search object:
IO 12602 2±0.5 4±0.4 4±0.3 8±0.7 5±0.8 10±0.9 7±0.8
ReCreateObject:
2.7 4.7 4.1 8.3 5.5 Null 7.6
ReCreateObject:
2.7 4.7 4.1 8.3 5.5 Null 7.6
ReCreateObject:
2.7 4.7 4.1 8.3 5.5 None 7.6
Search object:
Object absent!
The integrated results of the experiments allow us to draw conclusion about the effectiveness of the
input objects search in the information space for 7 parameters of information object and the length of
the interval of 5 units. They are presented in Table 2.
Table 2
Efficiency of search of input objects in information space at 7 parameters and length of an interval in
5 units
Probability that the parameter will not be read by Probability of identification of the input
sensors (NULL), % object, %
5 20
10 10
15 25
20 15
25 15
Table 2 shows the low level of the efficiency of input objects search in the information space for 7
parameters and for interval length of 5 units.
Next, we reduced the number of parameters that describe the information object, and with the same
initial data to repeat the similar experiments with the number of parameters from 4 to 6. The results of
the experiments are presented in Table 3.
Table 3
The efficiency for input objects search in the information space with an interval length of 5 units and
the number of parameters from 4 to 6
Probability of identification of the input
Probability that the parameter will not be read by object, %
sensors (NULL), % Number of parameters, units
4 5 6
5 100 100 65
10 100 100 45
15 100 90 85
20 100 100 75
25 100 100 50
Table 3 shows the 100% probability for the input object identification in the information space for
4 parameters and the length of the interval of 5 units, i.e. there is no need to reduce the number of
199
parameters. We design a graph on the data from Tables 2 and 3 in order to compare the efficiency of
search of input objects in the information space for an interval length of 5 units and the number of
parameters from 4 to 7 (Fig. 3).
Probability of identification of the input object,%
100
80
60
40
20
0
5 10 15 20 25
Probability that the parameter will not be read by sensors (NULL),%
4 5 6 7
Figure 3: Graph of comparison of efficiency of input objects search in information space for interval
length of 5 units and number of parameters from 4 to 7
Figure 3 shows the efficiency of the input objects search in the information space for an interval
length of 5 units has next average values: for 4 parameters – 100%; for 5 parameters – 98%; for 6
parameters – 64%; for 7 parameters – 17%.
We may conclude from the above-mentioned that the reducing the number of parameters leads to a
sharp increase in the efficiency of input objects search in the information space. Therefore, we decided
to change the parameter value intervals lengths of the source objects in the information space with a
constant value of the number of parameters.
Let us consider a case where each of the 20,000 information objects is described by 7 parameters
with an interval length of 3 to 5 units. The results of the experiments are presented in Table 4.
Table 4
The efficiency of input objects search in information space for 7 parameters and interval length from
3 to 5 units
Probability of identification of the input
Probability that the parameter will not be read by object, %
sensors (NULL),% The length of the interval, units
3 5 3
5 100 75 20
10 100 40 10
15 95 50 25
20 100 65 15
25 100 50 15
We may conclude that increasing the length of the interval results in a strong decrease in the
efficiency of input objects search in the information space. We design graph on the data in Table 4 in
order to compare the efficiency of search of input objects in the information space for 7 parameters and
the interval length from 3 to 5 units (Fig. 4).
200
Probability of identification of the input object,% 100
80
60
40
20
0
5 10 15 20 25
Probability that the parameter will not be read by sensors (NULL),%
3 4 5
Figure 4: Graph of comparison of efficiency of input objects search in information space for 7
parameters and interval length from 3 to 5 units
Figure 4 shows that the efficiency of search of input objects in the information space for 7 parameters
averaged: for interval length of 3 units – 99%; for interval length of 4 units – 56%; for interval length
of 5 units – 17%.
We decided to perform the similar experiments for a small intervals length of, but for more
parameters of the input objects in the information space.
We consider the case when every of the 20,000 information objects is described by 7 to 10
parameters with an interval length of 3 units. The results of the experiments are present in Table 5.
Table 5
Efficiency of input objects search in information space for 7 to 10 parameters and interval length of 3
units
Probability of identification of the input
Probability that the parameter will not be read by object, %
sensors (NULL), % Number of parameters, units
7 8 9 10
5 100 85 35 25
10 100 80 40 5
15 95 90 35 10
20 100 90 45 25
25 100 95 40 15
We design a graph on the data in Table 5 in order to compare the efficiency of search of input objects
in the information space for 7 to 10 parameters and the interval length of 3 units (Fig. 5).
201
100
Probability of identification of the input object,% 90
80
70
60
50
40
30
20
10
0
5 10 15 20 25
Probability that the parameter will not be read by sensors (NULL),%
7 8 9 10
Figure 5: Graph of comparison of efficiency of input objects search in information space for 7 to 10
parameters and interval length of 3 units
Figure 5 shows that the efficiency of search of input objects in the information space for interval
length of 3 units averaged: for 7 parameters – 99%; for 8 parameters – 88%; for 9 parameters – 39%;
for 10 parameters – 16%.
Experiments have shown that the probability of correct identification of the input object significantly
depends on the number of its parameters, as well as on the interval length of the parameters of the input
object.
Also, with the increase in the number of parameters of the source object and the intervals length of
object parameters, the efficiency of search of input objects in the information space decreases
significantly.
5. Comparison of the results obtained by machine learning and experimental
data
The adequacy of the suggested method for input objects identification in the information space was
checked using machine learning methods.
The multivariate logistic regression using the LogisticRegression class from the Scikit-Learn library
was used to identify information objects. By default, it applies a one-on-one strategy for more than two
classes.
To switch to multivariate logistic regression, the multi_class hyperparameter is set to “multinomial”
and the lbfgs solver is selected.
By default, we apply applied 𝑙 regularization, which can be controlled with the hyperparameter 𝐶.
X = info_data [“InformationObject”] # prepared parameters vectors for information objects
Y = info_data [“IdentifiedObject”]
softamx_reg = LogisticRegression (multi_class = “multinomial”, solver = “lbfgs”, C = 10)
softmax_reg.fit (X, Y)
This method was applied, for example, for the input object with the parameters [2,3 4,6 4,2 5,4 9,1
8,1] and the following information object was obtained with a probability of 94.2%:
IO 04011 2±0.4 4±0.7 4±0.2 5±0.5 9±0.4 9±0.9 8±0.4
202
Thus, we can conclude that the results obtained with the suggested method of input objects
identification in the information space are confirmed by the corresponding results obtained with the
machine learning method.
6. Conclusion
We develop an algorithm for the information space formation on the information system, which is
actually a software basis for maintaining the information space. Identification of the input object in the
information space allow us uniquely identify it by the appropriate parameters. To do this, we suggest
the method of identification based on step-by-step analysis of the object parameters.
References
[1] L. Zhu, C. Shi, and J. Guo, “Mapping discovery modeling and its empirical research for the
scientific and technological knowledge concept in unified concept space,” Cluster Computing,
2015, vol. 18 (1), pp. 103–112. doi:10.1007/s10586-013-0339-7
[2] L. Yakovis, “From unified information space to unified manufacturing control,” Automation and
Remote Control, 2016, vol. 77 (9), pp. 1689–1698. doi:10.1134/S0005117916090150
[3] S.A. Karin, “Integration in a single information space of diverse geospatial data,” Informatsionno-
upravlyayushchiye sistemy, 2012, vol. 2 (57), pp. 89–94. URL: http://www.i-
us.ru/index.php/ius/article/view/13797
[4] Ozhereleva T.A. On the relationship of the concepts information space, information field,
information environment and semantic environment. International Journal of Applied and
Fundamental Research, Vol. 10, N 2/2014. P. 21–24. [in Russian] URL: https://applied-
research.ru/ru/article/view?id=5989
[5] Tsvetkov V.Ya. Information field and information space. International Journal of Applied and
Fundamental Research, Vol.1, N 3/2016. 455 p. [in Russian] URL: https://applied-
research.ru/ru/article/view?id=8536
[6] A.V. Vincent, “Development of a model of a single information space to assess the reliability of
its operation,” Proceedings of the Kola Scientific Center of the Russian Academy of Sciences,
2011, vol. 7, pp. 65–70. URL: https://cyberleninka.ru/article/n/razrabotka-modeli-edinogo-
informatsionnogo-prostranstva-dlya-otsenki-nadezhnosti-ego-funktsionirovaniya
[7] A. Mayorov, “Information objects in the information field,” Educational Resources and
Technologies, 2015, vol. 1 (9), pp. 66–73. URL: https://vestnik-
muiv.ru/en/article/informatsionnye-obekty-v-informatsionnom-pole/
[8] K.V. Yakushenko, “Unified information space: theoretical approaches to the content of the
concept,” Belarus and world economic processes: Sat. scientific Art, 2014, vol. 11, p. 13–20. URL:
https://elib.bsu.by/bitstream/123456789/134114/1/13-20.pdf
[9] V.Ya. Tsvetkov, “Information interaction”, European Researcher, 2013, vol. 62, no. 11-1, pp.
2573–2577. URL: http://www.erjournal.ru/journals_n/1386019866.pdf
[10] V. Mukhin, Y. Komaga, V. Zavgorodnii, A. Zavgorodnya, O. Herasymenko, and O. Mukhin,
“Social Risk Assessment Mechanism Based on the Neural Networks,” IEEE International
Conference on Advanced Trends in Information Theory, 2019, pp. 179–182.
doi:10.1109/ATIT49449.2019.9030519
[11] H. Zhenbing, V. Mukhin, Ya. Kornaga, and O. Herasymenko, “Resource Management in a
Distributed Computer System with Allowance for the Level of Trust to Computational
Components”, Cybernetics and Systems Analysis, 2017, vol. 53 (2), pp. 312–322.
doi:10.1007/s10559-017-9931-9
[12] V. Mukhin, V. Zavgorodnii, Y. Kornaga, A. Zavgorodnya, I. Krylov, A. Rybalochka, V. Kornaga,
and R. Belous, “Devising a method to identify an incoming object based on the combination of
unified information spaces,” Eastern-European Journal of Enterprise Technologies, 2021, vol. 3,
no. 2(111), pp. 35–44. doi:10.15587/1729-4061.2021.229568
203
[13] M. Gershkovich, and T. Biryukova, “Problems of Identification of Information Objects in
Distributed Data Arrays,” Systems and Means of Inform., 2014, vol. 24(1), pp. 224–243.
doi:10.14357/08696527140114
[14] T. Wang, L. Liu, N. Liu, H. Zhang, L. Zhang, and S. Feng, “A multi-label text classification
method via dynamic semantic representation model and deep neural network,” Applied
Intelligence, 2020, vol. 50 (8), pp. 2339–2351. doi:10.1007/s10489-020-01680-w
[15] Y. Pan, G. Hu, J. Qiu, Y. Zhang, S. Wang, D. Shao, and Z. Pan, “FLGAI: a unified network
embedding framework integrating multi-scale network structures and node attribute information,”
Applied Intelligence, 2020, vol. 50 (11), pp. 3976–3989. doi:10.1007/s10489-020-01780-7
[16] WD Yu, ST Cheng, CM Wu, and HR Lou, “A self-evolutionary model for automated innovation
of construction technologies,” Automation in Construction, 2012, vol. 27, pp. 78–88.
doi:10.1016/j.autcon.2012.04.018
[17] G. Nagpal, M. Uddin, and A. Kaur, “A Comparative Study of Estimation by Analogy using Data
Mining Techniques,” JIPS, 2012, vol. 8 (4), pp. 621–652. doi:10.3745/JIPS.2012.8.4.621
[18] M. Arenas, BC Grau, E. Kharlamov, S. Marciuska, and D. Zheleznyakov, “Enabling Faceted
Search over OWL 2 with SemFacet,” In OWLED, 2014, pp. 121–132. URL:
https://marceloarenas.cl/publications/owled2014.pdf
[19] T. Teraoka, “Organization and exploration of heterogeneous personal data collected in daily life,”
Human-Centric Computing and Information Sciences, 2012, vol. 2 (1), pp. 1–15.
doi:10.1186/2192-1962-2-1
[20] RJ Little, and DB Rubin, “Statistical analysis with missing data”, 2019, P. 793. John Wiley &
Sons. doi:10.1002/9781119482260
[21] T. Cheng, HW Lauw, and S. Paparizos, “Entity synonyms for structured web searc,” IEEE
transactions on knowledge and data engineering, 2011, vol. 24 (10), pp. 1862–1875.
doi:10.1109/TKDE.2011.168
[22] P. Zuiev, R. Zhyvotovskyi, O. Zvieriev, S. Hatsenko, V. Kuprii, O. Nakonechnyi, M. Adamenko,
A. Shyshatskyi, Y. Neroznak, and V. Velychko, “Development of a complex methodology for
processing various types of data in intellectual systems to support decision-making,” Eastern-
European Journal of Enterprise Technologies, 2020, vol. 4, no. 9(106), pp. 14–23.
doi:10.15587/1729-4061.2020.208554
[23] V. Mukhin, V. Zavgorodnii, O. Barabash, R. Mykolaichuk, Y. Kornaga, A. Zavgorodnya, and V.
Statkevych, “Method of Restoring Parameters of Information Objects in a Unified Information
Space Based on Computer Networks,” International Journal of Computer Network and
Information Security, 2020, vol. 12 (2), pp. 11–21. doi:10.5815/ijcnis.2020.02.02
[24] M. Arenas, BC Grau, E. Kharlamov, Š. Marciuška, and D. Zheleznyakov, “Faceted search over
RDF-based knowledge graphs,” Journal of Web Semantics, 2016, vol. 37, pp. 55–74.
doi:10.1016/j.websem.2015.12.002
[25] BC Grau, E. Kharlamov, S. Marciuska, D. Zheleznyakov, and M. Arenas, “SemFacet: Faceted
Search over Ontology Enhanced Knowledge Graphs,” International Semantic Web Conference,
2016. URL: http://ceur-ws.org/Vol-1690/paper75.pdf
[26] S. Ferré, and A. Hermann, “Semantic search: Reconciling expressive querying and exploratory
search. In International semantic Web conference, Springer, Berlin, Heidelberg, 2011, pp. 177–
192. doi:10.1007/978-3-642-25073-6_12
[27] A. Dodonov, V. Mukhin, V. Zavgorodnii, Ya. Kornaga, and A. Zavgorodnya, “Method of
searching for information objects in unified information spac,” Systemresearch and information
technologies, 2021, vol. 1, pp. 34–46. doi:10.20535/SRIT.2308-8893.2021.1.03
[28] A.N. Logunov, and G.L. Logunova, “Choice of recognition attributes when searching hidden
objects,” Eastern-European Journal of Enterprise Technologies, 2012, vol. 5, no. 9(59), pp. 21–25.
doi:10.15587/1729-4061.2012.4622
[29] A. Dodonov, V. Mukhin, V. Zavgorodnii, Y. Kornaga, A. Zavgorodnya, and O. Mukhin, “Method
of Parallel Information Object Search in Unified Information Spaces,” International Journal of
Computer Network & Information Security, 2021, vol. 13 (4), pp. 1–13.
doi:10.5815/ijcnis.2021.04.01
[30] S. Ferré, A. Hermann, and M.Ducassé, “Semantic faceted search: Safe and expressive navigation
in RDF graphs,” [Research Report] PI 1964, 2011, pp.27. URL: https://hal.inria.fr/inria-00554093
204