У основі теоретичних і методологічних підходів до управління

N.O. Ivanchenko
Summary. In the article the feasibility of using MIVAR space to represent data and
knowledge about the state of human resources of economic security. It is proved that MIVAR space
will provide information on the status of human resources in the form of network models, semantic
networks and ontologies of the form of graphs that are placed in discrete multi-dimensional space.
Key words: MIVAR space, economic security, human resources, the semantic network.
Statement of the problem. Today human resources is one of the most
important factors ensuring economic security company (ESC) [5] . Thus , current
research focus of human resources is to use ESC MIVAR (Multidimensional
Informational Variable Adaptive) space, which will provide data and knowledge as
network models , semantic networks and ontologies that are kind of graphs , placed in
a discrete multidimensional space [2].
MIVAR space for the study of human resources ESC integrates and develops
the achievements in various fields of scientific databases, computational problems,
logical processing , and includes two main technologies:
1 ) MIVAR accumulation of information technology - a way of creating global
evolution of knowledge bases and rules with a variable structure based adaptive
discrete uniform information space of the data and rules based on three main concepts
"thing (thing), property relations ."
2 ) MIVAR processing technology - a way to create a system of inference or "
designing algorithms modules, services or procedures" based on the active network
trained MIVAR rules with linear computational complexity . MIVAR accumulation
of information technology is used to store any information on the possible
evolutionary variable structure without any restrictions on the scope and forms of
representation .
Analysis of recent research and publications. The theoretical work formed the
basis of domestic and foreign authors : G. Ivanchenko , A. Varlamov , E. Oleinikova ,
T. Gavrilova and others.
The purpose of the article. Rationale for use MIVAR space to study human
resources ESC as network models , semantic networks and ontologies that are kind of
graph placed in discrete multi-dimensional space.
The main material. One of the most important factors ensuring ESK is its
workforce. Therefore, it seems appropriate allocation of human resources and of the
individual assessment of economic security in this area . Sufficient supply businesses
need human resources and their rational use , high productivity have an indirect effect
on the level and neutralize threats ESK. In particular, the organization of the labor
process , choosing the optimal wage system , the establishment of social partnership
relations depend on the timeliness and quality of work , efficiency of the equipment
and as a result increase production, reduce its cost, profit growth , stabilization of the
financial situation of the company , that is, which determines its economic security.
First , MIVAR space representation of data about the state of human resources
ESC can work with dynamic ( evolutionary ) storage structures , which opens new
possibilities for creation of evolutionary cognitive subsystems for collecting and
processing information ESK. MIVAR space representation of the data allows implicit
associative various concepts and objects. This means that by analyzing the structure
of data storage will be through associative search to receive additional information
about the state of human resources ESC that is not contained explicitly in the
knowledge base. In addition, MIVAR space can introduce the concept of degree of
proximity - the distance between individual objects and between their clusters.
We offer the following approach for the establishment of management systems
- Focus on the semantic representation of knowledge that is completely
abstracted from the technical features of intelligent systems;
- Unification of models of intelligent subsystems ESC aimed at ensuring their
integrity ;
- Unit (Component , large- ) design based on libraries of standard components
used EKBP intelligent subsystems ;
- Gradual evolutionary design based on rapid prototyping ;
- Fully compatible with the design of tools designed system ;
- The inclusion of intelligent design technology integrated subsystems
intellectual help- system for developers of intelligent systems that significantly
reduce the initial requirements for their qualifications and thus greatly expand the
contingent of developers;
- The inclusion of intelligent systems designed help- subsystem -oriented
training end users , which greatly expand their contingent ;
- The inclusion of intelligent systems designed subsystems self-test ( self ,
introspection ) and sub -oriented automatic or automated as enhance their own
quality. This greatly improves the performance of intelligent support systems and
reduce the rate of obsolescence .
The basic idea of presenting potential ESC by MIVAR space and semantic
models is that the model represents the data ESC potentials and relations between
them explicit way that facilitates the access to knowledge , from the movement of
some concepts.
Network models , semantic networks and ontologies represented MIVAR space
in the form of graphs , placed into discrete multidimensional space, and only
enhances the possibility of such network models .
In terms of ESC this area includes not only the assessment of governance
structures , their composition and subordination , efficiency and consistency of
management decisions. A necessary condition for effective operation of the entity is
the rational construction of organizational and corporate structures, as well as
informatsiynoh software. Regarding the latter , it is the realization of the main
information resource aimed at improving economic management in all aspects of the
company to provide a comprehensive objective and timely information and
efektyvnoh use of human resources .
For human resources management ESC use the following parameters (Table 1).
Table 1
Name of the
indicator potential
1. High turnover of
Valid values
are indicators
d 1,1 – turnover of staff;
d 1,1 <1
d1,2 – amount released from all
(calculated in the
d1,1  1,2
d1,3 – average number
2. Stability
3. The level of
discipline (the
number of nonattendance at work)
d1,4 – tability (devotion) personnel;
d1,4 >0
d1,4 
d1,6 <1
d1,6 
d1,9 >0
d1,9 
d1,5 – total number of years of
employment of all staff
d1,6 – level of discipline (the number
of non-attendance at work);
d1,7 – absence to work (man-days);
d1,8 – average number of (man-days)
Matching d1,9 – compliance training to the
the degree of complexity of their work;
d1,10 – average tariff level of
complexity of their
d1,11 – average tariff level of work
that they perform
5. Ratio of specific d1,12 – ratio of specific categories of
of workers;
d1,13 – number of highly skilled and
qualified employees;
d1,14 – total number of employees;
d1,15 – number of key employees;
d1,16 – number of auxiliary workers;
 d1,13
 d1,14
 d
  1,15 ,
 d1,16
 1,17
 d1,18
d1,17 – number of workers employed
in production;
d1,18 – number of staff management
In most areas of human resources is essential to enterprise policy must include:
meeting the needs of the workforce on the state of the labor market (working
structure, age structure, turnover, regional features a set of necessary specialists, etc..)
Staff training, motivation and efficiency of productivity growth.
The article presents possible scenarios for the impact of human resources on
ESC (Table 2).
Table 2
Low level security
Medium security
High level of security
d 1,1
d 1,1 =0
d 1,1 >0
d 1,1 <1
d1,4 <0
d1,4 =0
d1,4 >0
d1,6 =0
d1,6 >0
d1,6 <1
d1,9 >0
d1,9 =0
d1,9 >0
To research human resources ESC proposed use MIVAR space that will
provide information about the state of human resources in the form of network
models , semantic networks and ontologies that are kind of graph placed in discrete
multidimensional space.
The basis MIVAR space to represent data and knowledge is holistic,
evolutionary , dynamic , multidimensional , and if necessary, object-oriented
representation of knowledge about the state of human resources ESC in which the
substance (thing) ( things , objects) , properties, and relationship can go at each other,
as the research subject , that nature may be the property of another entity or the entity
can be the attitude of other entities and vice versa.
Moreover , MIVAR space is based on the fact that users can simultaneously
use different data model from relational and hypertext , gradually introducing more
structured and going to networking , semantic networks and ontologies , and after
them - then to MIVAR space. MIVAR space is evolutionary and is designed for
changing data storage structures and transition to different models.
One of the components MIVAR space is a semantic network that depending
on the nature of the relationship , valid in them has a different nature. In situational
managing these relationships are mainly described the temporal, spatial and causal
relations between objects and results of operations on objects of party system
operates .
Semantic structure of a knowledge base ESC that describes some set ( initial)
visual area can be seen as a hierarchical system of different visual areas of special
type that superposed on the given subject domain. A specified atomic section given
subject domain , for example, can be visual area of human resources ESC . Designing
a knowledge base , which is a description of PWG can be roughly divided into the
following stages:
- Clarification of the structure described visual field;
- Clarification of the research subject ;
- Clarification of all signatures ;
- Specification of a set of auxiliary objects which communication is essential
to consider the objects ;
- Building a knowledge domain that is the set-theoretical ontology of the
subject domain ;
- Building a knowledge domain that is terminological ontology of the subject
domain ;
- Building a knowledge domain that is logical descriptions given subject
- Building a visual field of cognitive multimedia illustrations and
bibliographic sources for a given subject domain.
Thus, the design knowledge base can rozhlyadatt as the process of building
some initial knowledge domain and process knowledge domain extensions specified
by a number nadoblastey , each of which is a class of the objects.
The semantic structure of a knowledge base ESC (G)- structure is a sign that
there is a mathematical structure that is given by the five G = (VC, I, M, K), where V
- the set of vertices ( primary cells ).
C - many relationship ( secondary elements). Each ligament connects together
a number of elements of semantic web. These binding elements ( communications
components ) may be at the top, and the other links of the semantic web . Depending
on the number of binding elements ( components) connections can be binary , ternary
, 4 arnymy etc. As part of its communication components can be either different or
the same role. Depending on the connections are oriented and non-oriented .
I - family oriented binary incidence relations that bind ties with their semantic
network components. Thus various incidence relations define different roles of
components connection.
M - alphabet elements of the semantic web that is " syntactically " ( formally)
the allocation of salable different set of elements by " attributing " these elements of
many different marks M.
K - set of key nodes. Many of the sets M K differs only syntactic means
allocation of various subsets of the set (). The use of key nodes (set K) requires the
explicit introduction to many relationship (C ) communications devices , which
connects with the key nodes of the semantic network elements that belong to sets that
affect these key nodes.
Furthermore, this mathematical structure (G) must satisfy the semantic
- Top of the structure to be signs ( symbols ) described various objects;
- Communication of mathematical structures must be characters ( symbols )
of various bonds, which connects the described objects or links those described
objects associated with other bonds or links that connect the different connections ;
- The incidence of this mathematical relationship structures must be
characters ( symbols ) of different roles played by the various described objects or
links within those links, the components of which they are part;
- Alphabet elements of the mathematical structure should be treated as a
family of characters , each of which represents the appropriate type (class) of the
described objects and / or relationships;
- Within the framework of the mathematical structure among these symbols (
symbols) should be absent synonymous symbols , ie symbols that represent the same
thing ;
- Within the framework of the mathematical structure among these symbols (
symbols) should be absent homonymous signs that in different contexts, in different
circumstances may indicate a different meaning.
Many elements of the semantic network G = (V, C, I, M, K) is called the set
Generalized notion of graph structure can be considered as equivalent to the
concept of the Semantic Web as the top graph structure can hardly be synonymous or
omonimychnymy , although it is nowhere explicitly specified, because the semantics
of graph theory graph structure is not engaged . In fact, graph theory considers the
syntactic aspect of semantic networks , exploring different types of configurations up
to isomorphism . It is also clear that the semantic networks also include ways of
presenting information, such as cognitive maps, knowledge maps , and more . This is
particularly emphasize that the semantic network as an abstract mathematical
structure should be clearly distinguished from the different versions of the material
(physical) predstavlevlennya in computer memory, graphics rendering. Abstract
notion of semantic network that is precisely abstracted . Various semantic network
may have different alphabets elements ( different sets of labels on the elements of
semantic networks). Thus, we can speak of semantic networks are presented in
different alphabets , as well as different languages semantic networks , each of which
is associated with a fixed alphabet, which represented all the semantic network that
includes that language.
For all semantic networks kadrovoh potential ESC common declarative
graphical representation that can be used to represent knowledge and the
development of intelligent decision support systems based on knowledge.
Using semantic networks for knowledge representation on the situation of
human resources ESC important classification of object types and the allocation of
some basic types of connections between objects. Regardless of the features of the
environment that is modeled , we can assume that any more or less complex model it
reflects any generalized, concrete and aggregate facilities.
The basic idea of modeling capabilities ESC using semantic models is that the
model represents information about real-world objects and relations between them
explicit way that facilitates the access to knowledge movement from the beginning of
a term. Network models of knowledge representation ESC - this graph is usually
oriented, whose vertices correspond to certain concepts ( functional components ),
and the arc - the relation and communication between these components [7].The
results of the construction of semantic networks is presented in Figure 1 , 2.
Figure. 1 Scheme of semantic networks with hierarchical arrangement of
vertices of human resources ESC
Nodes (vertices networks) are some concepts (objects, events, phenomena)
knowledge of the PWG and the arcs relations among them. Semantic model is objectoriented and provide sufficiently such features as connectivity, implementing four
such relationships between objects ESC: classification, aggregation, generalization,
Figure . 2. Figure semantic network nodes with a human resources and impact
of information and potential analitychnho
At various syntactic restrictions on the structure of the semantic network
having rigid types of representations. For example, the ontological representation
characteristic of ontologies or causal representation in logic that are widespread in
machine methods of inference or logic programming languages .
Conclusion. Use MIVAR space can provide the knowledge base ESC
knowledge of human resources and implement the automatic construction of
semantic networks. Imposing restrictions on the description of the top arcs, you can
get a network of different types . One of the main differences between hierarchical
semantic networks of human resources ESC from simple possibility is to divide the
network into subnets , and establish relationships not only between facts and events.
A characteristic feature of network models of knowledge of functional
capabilities ESC is an integrated description of the procedural and static semantics of
admissible operations on objects defined in conjunction with the definition of data
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