Information resources distribution between automated workstations in local corporative networks
This article focuses on the problem of optimal distribution of related information resources between automated workstations in local corporate networks. In this work we present a mathematical description of the algorithm for quasi-optimal distribution of related information resources at designing au...
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2023
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pp_isofts_kiev_ua-article-5032023-06-24T10:04:15Z Information resources distribution between automated workstations in local corporative networks Розподіл інформаційних ресурсів між автоматизованими робочими місцями в локальних корпоративних мережах Pursky, O.I. Kozlov, V. V. Tomashevska, T.V. Dyvak, V.V. Hordiiko, N. O. Sinitsky, M.Y. data distribution; local networks; quasi-optimal distribution algorithm; automated workstations UDC 004.75; 004.77; 004.9 розподіл даних; локальні мережі; алгоритм квазіоптимального розподілу; автоматизовані робочі станції УДК 004.75; 004.77; 004.9 This article focuses on the problem of optimal distribution of related information resources between automated workstations in local corporate networks. In this work we present a mathematical description of the algorithm for quasi-optimal distribution of related information resources at designing automated workstations in a local corporate network. The undirected graph describing the task of information resources optimal distribution is presented. The method of quasi-optimal distribution of related resources at designing automated workstations in the local corporative network is proposed based on the developed algorithm. Using conditional organization as an example the modeling of optimal distribution of related information resources has been considered in local corporative network. The described algorithm provides an opportunity to optimally distribute the information resource in the local corporate network, as well as solve the task of building reliable and efficient local networks. The proposed method of quasi-optimal distribution of related information resources can be used in corporation of any type.Problems in programming 2022; 2: 23-31 В статті розглядається проблематика оптимального розподілу пов’язаних інформаційних ресурсів між автоматизованими робочими станціями в локальних корпоративних мережах. В роботі представлено математичний опис алгоритму квазіоп- тимального розподілу пов’язаних інформаційних ресурсів при проектуванні автоматизованих робочих станцій в локальній корпоративній мережі. Наведено граф опису задачі оптимального розподілу ресурсів. На підставі розробленого алгоритму пропонується методика квазіоптимального розподілу пов’язаних ресурсів при проектуванні автоматизованих робочих стан- цій в локальній мережі організацій. Здійснено моделювання оптимального розподілу інформаційних ресурсів на прикладі умовної організації. Використання описаного алгоритму дозволяє оптимально розподіляти інформаційний ресурс в локальній корпоративній мережії, а також вирішувати питання побудови локальних мереж з високою надійністю та ефективністю ви- користання. Запропонована методика квазіоптимального розподілу пов’язаних інформаційних ресурсів може застосовуватися в організаціях будь-якого рівня.Problems in programming 2022; 2: 23-31 Інститут програмних систем НАН України 2023-01-23 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/503 10.15407/pp2022.03-04.023 PROBLEMS IN PROGRAMMING; No 3-4 (2022); 23-31 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 3-4 (2022); 23-31 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 3-4 (2022); 23-31 1727-4907 10.15407/pp2022.03-04 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/503/554 Copyright (c) 2023 PROBLEMS IN PROGRAMMING |
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2023-06-24T10:04:15Z |
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data distribution local networks quasi-optimal distribution algorithm automated workstations UDC 004.75 004.77 004.9 |
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data distribution local networks quasi-optimal distribution algorithm automated workstations UDC 004.75 004.77 004.9 Pursky, O.I. Kozlov, V. V. Tomashevska, T.V. Dyvak, V.V. Hordiiko, N. O. Sinitsky, M.Y. Information resources distribution between automated workstations in local corporative networks |
| topic_facet |
data distribution local networks quasi-optimal distribution algorithm automated workstations UDC 004.75 004.77 004.9 розподіл даних локальні мережі алгоритм квазіоптимального розподілу автоматизовані робочі станції УДК 004.75 004.77 004.9 |
| format |
Article |
| author |
Pursky, O.I. Kozlov, V. V. Tomashevska, T.V. Dyvak, V.V. Hordiiko, N. O. Sinitsky, M.Y. |
| author_facet |
Pursky, O.I. Kozlov, V. V. Tomashevska, T.V. Dyvak, V.V. Hordiiko, N. O. Sinitsky, M.Y. |
| author_sort |
Pursky, O.I. |
| title |
Information resources distribution between automated workstations in local corporative networks |
| title_short |
Information resources distribution between automated workstations in local corporative networks |
| title_full |
Information resources distribution between automated workstations in local corporative networks |
| title_fullStr |
Information resources distribution between automated workstations in local corporative networks |
| title_full_unstemmed |
Information resources distribution between automated workstations in local corporative networks |
| title_sort |
information resources distribution between automated workstations in local corporative networks |
| title_alt |
Розподіл інформаційних ресурсів між автоматизованими робочими місцями в локальних корпоративних мережах |
| description |
This article focuses on the problem of optimal distribution of related information resources between automated workstations in local corporate networks. In this work we present a mathematical description of the algorithm for quasi-optimal distribution of related information resources at designing automated workstations in a local corporate network. The undirected graph describing the task of information resources optimal distribution is presented. The method of quasi-optimal distribution of related resources at designing automated workstations in the local corporative network is proposed based on the developed algorithm. Using conditional organization as an example the modeling of optimal distribution of related information resources has been considered in local corporative network. The described algorithm provides an opportunity to optimally distribute the information resource in the local corporate network, as well as solve the task of building reliable and efficient local networks. The proposed method of quasi-optimal distribution of related information resources can be used in corporation of any type.Problems in programming 2022; 2: 23-31 |
| publisher |
Інститут програмних систем НАН України |
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2023 |
| url |
https://pp.isofts.kiev.ua/index.php/ojs1/article/view/503 |
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23
Теоретичні і методологічні основи програмування
UDC 004.75; 004.77; 004.9 https://doi.org/10.15407/pp2022.03-04.023
INFORMATION RESOURCES
DISTRIBUTION BETWEEN AUTOMATED
WORKSTATIONS IN LOCAL
CORPORATIVE NETWORKS
Oleg Pursky, Valery Kozlov, Tetyana Tomashevska,
Volodymyr Dyvak, Nataliia Hordiiko, Mykola Sinitsky
This article focuses on the problem of optimal distribution of related information resources between automated workstations in local
corporate networks. In this work we present a mathematical description of the algorithm for quasi-optimal distribution of related
information resources at designing automated workstations in a local corporate network. The undirected graph describing the task of
information resources optimal distribution is presented. The method of quasi-optimal distribution of related resources at designing
automated workstations in the local corporative network is proposed based on the developed algorithm. Using conditional organization
as an example the modeling of optimal distribution of related information resources has been considered in local corporative network.
The described algorithm provides an opportunity to optimally distribute the information resource in the local corporate network, as
well as solve the task of building reliable and efficient local networks. The proposed method of quasi-optimal distribution of related
information resources can be used in corporation of any type.
Keywords: data distribution, local networks, quasi-optimal distribution algorithm, automated workstations..
В статті розглядається проблематика оптимального розподілу пов’язаних інформаційних ресурсів між автоматизованими
робочими станціями в локальних корпоративних мережах. В роботі представлено математичний опис алгоритму квазіоп-
тимального розподілу пов’язаних інформаційних ресурсів при проектуванні автоматизованих робочих станцій в локальній
корпоративній мережі. Наведено граф опису задачі оптимального розподілу ресурсів. На підставі розробленого алгоритму
пропонується методика квазіоптимального розподілу пов’язаних ресурсів при проектуванні автоматизованих робочих стан-
цій в локальній мережі організацій. Здійснено моделювання оптимального розподілу інформаційних ресурсів на прикладі
умовної організації. Використання описаного алгоритму дозволяє оптимально розподіляти інформаційний ресурс в локальній
корпоративній мережії, а також вирішувати питання побудови локальних мереж з високою надійністю та ефективністю ви-
користання. Запропонована методика квазіоптимального розподілу пов’язаних інформаційних ресурсів може застосовуватися
в організаціях будь-якого рівня.
Ключові слова: розподіл даних, локальні мережі, алгоритм квазіоптимального розподілу, автоматизовані робочі станції.
Introduction
At present, the most peculiarities in corporations activity are the wide use of IT and the effective distribution
of information resources within their organizational structures [1, 2]. Networking distributed technologies with high
bandwidth are becoming the main channels for the exchange of goods and services contributes to the emergence
of new business structures, which through information network system establish partnerships and carry out their
economic activities [3, 4]. Most modern applied information technologies that used in business are based on dis-
tributed systems of information processing [5-8]. Currently, we can observe a tendency to allocate and decentralize
computing resources.
The use of distribution of related information resources network technologies in everyday business has
become a norm for western companies. The distributed network technologies are used not only for messaging and
for access to information resources, but also for carrying out specific commercial transactions. The development of
such technologies helps to reduce costs, accelerate all business processes and, as a result, increase the profitability
of the company’s operations. The high-technologies degree and practical implementation of network technology
of distribution of related information resources in any modern enterprise considerably determines its success on
electronic market. The level of distributed network application directly determines the competitiveness of its goods
and services.
Manufacturing companies of hardware and software have been the first to use the distributed network tech-
nologies at goods and services marketing and distribution. An example of successful development of many of them
has become a factor of attractiveness for businesses operating in other areas. Accumulated experience of distribution
of related information resources network technologies is generalized, even the scientific researchers are conducted
and materials are published [1, 3, 5, 6].
The technological aspects of modern economic activity of corporations are extremely important factors that
ensure the effectiveness of its operation. The reliability, safety of technological solutions are the basis of economic
activity, which depends on the effective distribution of related information resources. The current level of technolo-
gy development allows creating high-performance, protected from external interference information and communi-
cation means of distribution of related information resources. There is also a certain list of organizations that support
the reliable operation of technologies as the main means of information resources distribution of enterprise [3, 5].
© О.І. Пурський, В.В. Козлов, Т.В. Томашевська, Н.О. Гордійко, В.В. Дивак, М.Є. Сіницький, 2022
ISSN 1727-4907. Проблеми програмування. 2022. № 3-4. Спеціальний випуск
24
Теоретичні і методологічні основи програмування
The allocation of computing resources is followed by much greater management decentralization than it
could be usually observed in centralized environment, when the data center had strong control over the resources
of a large computing system. One of the positive aspects of decentralization is the higher degree of dynamics in
multiple areas of computer equipment usage, such as installation and development of applications, connection of
peripherals, system expansion, etc.
One of the promising methods to increase the resource management efficiency is implementation
of the techniques and tools of new information technology in the performance of the officials. At present
the computer, used by authorities as a tool of individual automatization, should be considered as one of the
centers of complex technological process of organization document flows of relevant information. The con-
tinuity and independence of this technological process requires close attention to the optimization of com-
munication between official’s automated workstations, which use local area networks (LANs).
In this work LAN is addressed as a distributed computing system built on the basis of a common data trans-
mission environment (local area network), which provides physical connectivity of all system components, easy
system reconfiguration and comprehensive coverage of its structural elements.
The usage of computer networks in organizations allows implementing real-time information links between
official structure element, providing efficient and distributed data processing.
Distributed database (DB) is a set of logically interconnected databases, distributed via the computer net-
work [9, 10]. Their creation and maintenance on the basis of LAN allows to automatically implementing optimal,
from the standpoint of some particular criteria, filling out of the formalized documents, in the development of which
many officials, departments and services are involved.
In general, a Distributed Data Base (DDB) is typically a collection of databases (DBs) that includes frag-
ments of many databases that are located on the different nodes in a computer network and are managed by different
management systems (DBMS), still remaining available for sharing in different applications (Fig. 1). A distributed
database looks like a regular local database to users and applications. In this sense, the term “distributed” reflects
the way the database is organized, but not its external characteristics (“distribution” of the database is invisible from
the outside) [9, 10].
Теоретичні і методологічні основи програмування
computer equipment usage, such as installation and development of applications, connection of peripherals, system
expansion, etc.
One of the promising methods to increase the resource management efficiency is implementation of the
techniques and tools of new information technology in the performance of the officials. At present the computer, used
by authorities as a tool of individual automatization, should be considered as one of the centers of complex
technological process of organization document flows of relevant information. The continuity and independence of this
technological process requires close attention to the optimization of communication between official’s automated
workstations, which use local area networks (LANs).
In this work LAN is addressed as a distributed computing system built on the basis of a common data
transmission environment (local area network), which provides physical connectivity of all system components, easy
system reconfiguration and comprehensive coverage of its structural elements.
The usage of computer networks in organizations allows implementing real-time information links between
official structure element, providing efficient and distributed data processing.
Distributed database (DB) is a set of logically interconnected databases, distributed via the computer network [9,
10]. Their creation and maintenance on the basis of LAN allows to automatically implementing optimal, from the
standpoint of some particular criteria, filling out of the formalized documents, in the development of which many
officials, departments and services are involved.
In general, a Distributed Data Base (DDB) is typically a collection of databases (DBs) that includes fragments of
many databases that are located on the different nodes in a computer network and are managed by different
management systems (DBMS), still remaining available for sharing in different applications (Fig. 1). A distributed
database looks like a regular local database to users and applications. In this sense, the term "distributed" reflects the
way the database is organized, but not its external characteristics ("distribution" of the database is invisible from the
outside) [9, 10].
Fig. 1. General view of the distributed database
The usage of computer networks allows implementing information links between elements of the structure of the
governing body, including officials, providing prompt and distributed data processing.
Fig. 1. General view of the distributed database
The usage of computer networks allows implementing information links between elements of the structure
of the governing body, including officials, providing prompt and distributed data processing.
Local area networks, having the ability to configure-to-order, allow forming necessary structure of discrete
physical elements and processes. This allows fitting into the existing organizational and staffing structure in the
early stages of management automatization.
25
Теоретичні і методологічні основи програмування
Local area networks are an integral part of distributed in time and space information processing and
storage systems. Analysis of foreign literature has shown that local area networks are the main component
of information systems during the automatization of managerial work [11, 12]. The main advantage of such
networks is the distribution and integration of resources of spatially allocated computers, and at the same
time the ability to maintain the information interaction between officials (operators). The optimal distribu-
tion of information resources between the workstations is an issue, the solution of which will reduce the time
of the decision-making process.
Method
This section presents a method for determining the quasi-optimal distribution of related information re-
sources at designing automated workstations in a local corporate network. Let us directly consider the mathematical
allgorithm and method of information resources optimal distribution.
Data set di∈D, i = 1, 2, ...,m, used by N operators, frequency matrix F = ║fij║ (i = 1, 2, ..., m, j = 1, 2, ..., N) of
data usage by officials (estimated data flow, unit of measurement - for example, bod). The data should be distributed
over sets Oj(j = 1, 2, ...,N), which form a distributed database of the organization O = O1∪O2∪...∪Oj∪...∪ON(Oi∩Oj
= ∅fori≠j).
Each set Oj is located in the base computer of the user (operator) oj, the computers are connected to
the local network of the organization. There is given a matrix of communication values С = ║сkj║ (k = 1, 2,
...,N; j = 1, 2, ..., N) of the operator k with the “outlying” database j (ckk= 0) –so fee for communication via the
network is known.
It is necessary to find a distribution of elements of the set D between sets Oj, which minimizes the total loss
from the failed distribution:
Теоретичні і методологічні основи програмування
[Введите текст]
Local area networks, having the ability to configure-to-order, allow forming necessary structure of discrete
physical elements and processes. This allows fitting into the existing organizational and staffing structure in the early
stages of management automatization.
Local area networks are an integral part of distributed in time and space information processing and storage
systems. Analysis of foreign literature has shown that local area networks are the main component of information
systems during the automatization of managerial work [11, 12]. The main advantage of such networks is the distribution
and integration of resources of spatially allocated computers, and at the same time the ability to maintain the
information interaction between officials (operators). The optimal distribution of information resources between the
workstations is an issue, the solution of which will reduce the time of the decision-making process.
Method
This section presents a method for determining the quasi-optimal distribution of related information resources at
designing automated workstations in a local corporate network. Let us directly consider the mathematical allgorithm
and method of information resources optimal distribution.
Data set diD, i = 1, 2, ...,m, used by N operators, frequency matrix F = ║fij║ (i = 1, 2, ..., m, j = 1, 2, ..., N) of
data usage by officials (estimated data flow, unit of measurement - for example, bod). The data should be distributed
over sets Oj(j = 1, 2, ...,N), which form a distributed database of the organization O = O1O2...Oj...ON(OiOj =
forij).
Each set Oj is located in the base computer of the user (operator) oj, the computers are connected to the local
network of the organization. There is given a matrix of communication values С = ║сkj║ (k = 1, 2, ...,N; j = 1, 2, ..., N)
of the operator k with the "outlying" database j (ckk= 0) –so fee for communication via the network is known.
It is necessary to find a distribution of elements of the set D between sets Oj, which minimizes the total loss from
the failed distribution:
,min
1 1 1
= = =
→=
N
k
N
j
m
i
ijjkij xcfS (1)
Where xij is the membership function, which is defined as follows:
,
1
0
=
ji
ji
ij Od
Od
x (2)
It is clear that in this case
=
=
N
j
ijx
1
.1
1. Consider the case when all cij= c (for ij).
The problem has a trivial solution if in the matrix F all elements of a column g are maximal in their rows, so the
predicate gij{figfij (jg)} is valid. In this case, all elements of the set D must be placed in Og.
The graph that describes the problem G = (X, U) contains vertices of two types (data d1, d2, ...,dmD and
operators o1, o2, ..., oN O), ieX = D O, and is dicotyledonous (Fig. 2).
(1)
Where xij is the membership function, which is defined as follows:
Теоретичні і методологічні основи програмування
[Введите текст]
Local area networks, having the ability to configure-to-order, allow forming necessary structure of discrete
physical elements and processes. This allows fitting into the existing organizational and staffing structure in the early
stages of management automatization.
Local area networks are an integral part of distributed in time and space information processing and storage
systems. Analysis of foreign literature has shown that local area networks are the main component of information
systems during the automatization of managerial work [11, 12]. The main advantage of such networks is the distribution
and integration of resources of spatially allocated computers, and at the same time the ability to maintain the
information interaction between officials (operators). The optimal distribution of information resources between the
workstations is an issue, the solution of which will reduce the time of the decision-making process.
Method
This section presents a method for determining the quasi-optimal distribution of related information resources at
designing automated workstations in a local corporate network. Let us directly consider the mathematical allgorithm
and method of information resources optimal distribution.
Data set diD, i = 1, 2, ...,m, used by N operators, frequency matrix F = ║fij║ (i = 1, 2, ..., m, j = 1, 2, ..., N) of
data usage by officials (estimated data flow, unit of measurement - for example, bod). The data should be distributed
over sets Oj(j = 1, 2, ...,N), which form a distributed database of the organization O = O1O2...Oj...ON(OiOj =
forij).
Each set Oj is located in the base computer of the user (operator) oj, the computers are connected to the local
network of the organization. There is given a matrix of communication values С = ║сkj║ (k = 1, 2, ...,N; j = 1, 2, ..., N)
of the operator k with the "outlying" database j (ckk= 0) –so fee for communication via the network is known.
It is necessary to find a distribution of elements of the set D between sets Oj, which minimizes the total loss from
the failed distribution:
,min
1 1 1
= = =
→=
N
k
N
j
m
i
ijjkij xcfS (1)
Where xij is the membership function, which is defined as follows:
,
1
0
=
ji
ji
ij Od
Od
x (2)
It is clear that in this case
=
=
N
j
ijx
1
.1
1. Consider the case when all cij= c (for ij).
The problem has a trivial solution if in the matrix F all elements of a column g are maximal in their rows, so the
predicate gij{figfij (jg)} is valid. In this case, all elements of the set D must be placed in Og.
The graph that describes the problem G = (X, U) contains vertices of two types (data d1, d2, ...,dmD and
operators o1, o2, ..., oN O), ieX = D O, and is dicotyledonous (Fig. 2).
(2)
It is clear that in this case
Теоретичні і методологічні основи програмування
[Введите текст]
Local area networks, having the ability to configure-to-order, allow forming necessary structure of discrete
physical elements and processes. This allows fitting into the existing organizational and staffing structure in the early
stages of management automatization.
Local area networks are an integral part of distributed in time and space information processing and storage
systems. Analysis of foreign literature has shown that local area networks are the main component of information
systems during the automatization of managerial work [11, 12]. The main advantage of such networks is the distribution
and integration of resources of spatially allocated computers, and at the same time the ability to maintain the
information interaction between officials (operators). The optimal distribution of information resources between the
workstations is an issue, the solution of which will reduce the time of the decision-making process.
Method
This section presents a method for determining the quasi-optimal distribution of related information resources at
designing automated workstations in a local corporate network. Let us directly consider the mathematical allgorithm
and method of information resources optimal distribution.
Data set diD, i = 1, 2, ...,m, used by N operators, frequency matrix F = ║fij║ (i = 1, 2, ..., m, j = 1, 2, ..., N) of
data usage by officials (estimated data flow, unit of measurement - for example, bod). The data should be distributed
over sets Oj(j = 1, 2, ...,N), which form a distributed database of the organization O = O1O2...Oj...ON(OiOj =
forij).
Each set Oj is located in the base computer of the user (operator) oj, the computers are connected to the local
network of the organization. There is given a matrix of communication values С = ║сkj║ (k = 1, 2, ...,N; j = 1, 2, ..., N)
of the operator k with the "outlying" database j (ckk= 0) –so fee for communication via the network is known.
It is necessary to find a distribution of elements of the set D between sets Oj, which minimizes the total loss from
the failed distribution:
,min
1 1 1
= = =
→=
N
k
N
j
m
i
ijjkij xcfS (1)
Where xij is the membership function, which is defined as follows:
,
1
0
=
ji
ji
ij Od
Od
x (2)
It is clear that in this case
=
=
N
j
ijx
1
.1
1. Consider the case when all cij= c (for ij).
The problem has a trivial solution if in the matrix F all elements of a column g are maximal in their rows, so the
predicate gij{figfij (jg)} is valid. In this case, all elements of the set D must be placed in Og.
The graph that describes the problem G = (X, U) contains vertices of two types (data d1, d2, ...,dmD and
operators o1, o2, ..., oN O), ieX = D O, and is dicotyledonous (Fig. 2).
1. Consider the case when all cij= c (for i≠j).
The problem has a trivial solution if in the matrix F all elements of a column g are maximal in their rows, so
the predicate ∃g∀i∀j{fig≥fij (j≠g)} is valid. In this case, all elements of the set D must be placed in Og.
The graph that describes the problem G = (X, U) contains vertices of two types (data d1, d2, ...,dm∈D and
operators o1, o2, ..., oN∈ O), ieX = D∪ O, and is dicotyledonous (Fig. 2).
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
Fig. 2. Graph of the task description
26
Теоретичні і методологічні основи програмування
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices in-
cluded in the set D:
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined (
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
.
Data
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
for which
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
, is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
. The procedure
is repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di
does not matter, so for each di in its tuple
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
is determined and
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
is attributed to the set of
operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
. Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
(4)
In the general case, we have a loss matrix
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
Теоретичні і методологічні основи програмування
Fig. 2. Graph of the task description
Since the graph is undirected, its description by tuples [10] should be given relatively to the vertices included
in the set D:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>>}f ;o< ..., >,f ;o< ..., >,f ;o< >,f ;o{< N, ,d<
>}>; f>, ..., <o; f>, ..., <o; f>, <o; f, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(3)
The key idea of the algorithm for the distribution of vertices included in the data set D between N sets, which
are formed near the operator vertices oj, is: the maximum value of fij for all i and j is determined ( ) max(
ij
*
** ijji ffij = .
Data *id for which i=i*, j=j* ( *
** jiij ff = ), is attributed to the operator set oj (j = j*).
The tuple di (i=i*) is removed from the description of the graph di (i=i*): *\1
idDD = .. The procedure is
repeated until all data is deleted. Since all data must be distributed, the order of processing tuples for di does not matter,
so for each di in its tuple
jij
df 32j
* PrPr max* = is determined and id is attributed to the set of operators j*.
It is better to describe the result in tuples relatively to the vertices included in the set O.
2. General case.
Obviously, when assigning a given d1 to the set o1, the partial loss due to such an assignment is s11=
=
N
j
jjcf
1
11
.
Similarly, for an arbitrary data element di its assignment to the set Oj leads to losses:
=
=
N
k
kiikij cfs
1
(4)
In the general case, we have a loss matrix .,1 ,, NjNiisij ==
kjikij cfs = (5)
(5)
The algorithm, which is described above,is focused on using the matrix
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
,and it can be applied to using
the matrix
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
. The graph is described by tuples:
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
). Дані
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
, для яких i=i*, j=j*
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
The procedure is repeated until all data is removed. For each di
in its tuple
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
is determined and
Теоретичні і методологічні основи програмування
[Введите текст]
The algorithm, which is described above,is focused on using the matrix
ijf ,and it can be applied to using
the matrix
ijs . The graph is described by tuples:
,
...,...,...,...,...,...,...
...,...,...,...,...,...,...
...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
,...,...,...,...,...,...,...
111221111
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>>} s;o< ..., >, s;o< ..., >, s;o< >, s;o{< N, ,d<
>}>; s>, ..., <o; s>, ..., <o; s>, <o; s, N, {<o<d
mNNmiim22m11m
jNNjiij22j11j
NNii
(6)
In contrast to the algorithm of item 1 in this algorithm minimum value of sij for all i and j is determined (
) min(
ij
*
** ijji
ssij = ). Дані *id , для яких i=i*, j=j* ( *
** jiij ss = ), are assigned to the set of operator oj (j=j*).
Next, the algorithm completely coincides with the algorithm described in item 1: the tuple di (i = i*) is
removed from the graph description:
*\1
i
dDD = The procedure is repeated until all data is removed. For each di in its
tuple
jij
ds 32j
* PrPr min* = is determined and
id is attributed to the set Oj*. In accordance with the mentioned above a
method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the corporation is
proposed (Fig. 3).
is attributed to the set Oj*. In accordance with the mentioned
above a method of quasi-optimal distribution of related resources in the design of workstations in the LAN of the
corporation is proposed (Fig. 3).
27
Теоретичні і методологічні основи програмування Теоретичні і методологічні основи програмування
Fig. 3. Method of quasi-optimal distribution of related resources when designing of workstations in the LAN.
Results and discussion
This section presents the results of modeling of optimal distribution of related information resources using the
conditional organization as an example.
To explain the algorithm application the example of the simulated organization is considered. Analysis of the
organizational and staffing structure of the organization official, the tasks to be solved by departments during the
preparation of activities allowed to identify groups of users of the distributed database (Tab. 1).
Fig. 3. Method of quasi-optimal distribution of related resources when designing
of workstations in the LAN.
Results and discussion
This section presents the results of modeling of optimal distribution of related information resources using
the conditional organization as an example.
To explain the algorithm application the example of the simulated organization is considered. Analysis of
the organizational and staffing structure of the organization official, the tasks to be solved by departments during the
preparation of activities allowed to identify groups of users of the distributed database (Tab. 1).
28
Теоретичні і методологічні основи програмування
Table 1. The set of user groups of the considered information system of the organization О = {Оj}
Users (operators) Legend
Management group О1
Group 1 О2
Group2 О3
Group3 О4
Group4 О5
Group5 О6
Group6 О7
Group7 О8
Group8 О9
Group9 О10
Group10 О11
Continuous extraction, collection, analysis, generalization and evaluation of situation data under any cir-
cumstances aimed at ensuring that the chief makes the justified decision, timely clarification during the work, tak-
ing into consideration the changed situation, as well as quality implementation of all other measures to manage the
organization.
To manage its activity the organization of any level must have data:
− about competitors;
− expected demand for products;
− the number of products sold for a certain period of time;
− the amount of raw materials from the supplier at the beginning of the working day;
− average daily load;
− the economic condition of the location;
− the social composition of local population;
− the peculiarities of the time of year and day;
− address of supplier or buyer, etc.
Based on the study, five sets of the information elements describing a subject area of users were identified
in the information system of the organization:
D1 – a set of information elements (IE), which describes the necessary data about competitors;
D2 – a set of IE, containing the necessary information about organization capabilities;
D3 – a set of IE, that includes the necessary data on the capabilities of competitors;
D4 – set of IE, that describes the geographical required data;
D5 – set of IE, containing the data on the external environment (social-economic, political et cet.)
The set of information elements D = {dj}, that describes the subject area of the set of users O = {oj}, is ob-
tained by combining the sets D1, D2, D3, D4, D5:
Теоретичні і методологічні основи програмування
[Введите текст]
Table 1
The set of user groups of the considered information system of the organization О = {Оj}
Users (operators) Legend
Management group О1
Group 1 О2
Group2 О3
Group3 О4
Group4 О5
Group5 О6
Group6 О7
Group7 О8
Group8 О9
Group9 О10
Group10 О11
Continuous extraction, collection, analysis, generalization and evaluation of situation data under any
circumstances aimed at ensuring that the chief makes the justified decision, timely clarification during the work, taking
into consideration the changed situation, as well as quality implementation of all other measures to manage the
organization.
To manage its activity the organization of any level must have data:
− about competitors;
− expected demand for products;
− the number of products sold for a certain period of time;
− the amount of raw materials from the supplier at the beginning of the working day;
− average daily load;
− the economic condition of the location;
− the social composition of local population;
− the peculiarities of the time of year and day;
− address of supplier or buyer, etc.
Based on the study, five sets of the information elements describing a subject area of users were identified in
the information system of the organization:
D1 – a set of information elements (IE), which describes the necessary data about competitors;
D2 – a set of IE, containing the necessary information about organization capabilities;
D3 – a set of IE, that includes the necessary data on the capabilities of competitors;
D4 – set of IE, that describes the geographical required data;
D5 – set of IE, containing the data on the external environment (social-economic, political et cet.)
The set of information elements D = {dj}, that describes the subject area of the set of users O = {oj}, is
obtained by combining the sets D1, D2, D3, D4, D5:
jdDDDDDD == 54321 (7)
Officials of the organization (groups of operators of the information system) for supplying their work use
different IE. The frequency matrix F = ║fij║ (i = 1, 2, ...,m, j = 1, 2, ..., N) of data usage by officials is presented in
Table 2, which reproduces the number of appeals to these data per unit time (day).
Table 2
Frequency matrix F of data usage by organization officials
F=
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
D1 175 127 91 20 30 10 107 118 81 62 132
D2 120 123 111 104 90 74 40 75 60 73 10
(7)
Officials of the organization (groups of operators of the information system) for supplying their work use
different IE. The frequency matrix F = ║fij║ (i = 1, 2, ...,m, j = 1, 2, ..., N) of data usage by officials is presented in
Table 2, which reproduces the number of appeals to these data per unit time (day).
Table 2. Frequency matrix F of data usage by organization officials
F=
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
D1 175 127 91 20 30 10 107 118 81 62 132
D2 120 123 111 104 90 74 40 75 60 73 10
D3 27 29 19 10 3 2 20 15 19 4 30
D4 56 59 43 6 18 4 27 37 30 12 30
D5 2 11 19 0 2 3 11 20 11 8 18
The matrix of values С = ║сkj║ (k = 1, 2, ..., N; j = 1, 2, ..., N) communication of the operator k with the
“outlying” database j (ckk = 0) is given in Table 3:
29
Теоретичні і методологічні основи програмування
Table 3. Matrix С of values of operator communication
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
O1 0 0,067 0,04 0,013 0,018 0,015 0,013 0,012 0,011 0,015 0,014
O2 0,067 0 0,025 0,011 0,017 0,013 0,011 0,011 0,011 0,013 0,013
O3 0,04 0,025 0 0,02 0,067 0,05 0,033 0,026 0,025 0,029 0,017
O4 0,013 0,011 0,02 0 0,022 0,025 0,04 0,05 0,05 0,018 0,04
O5 0,018 0,017 0,067 0,022 0 0,029 0,018 0,014 0,014 0,05 0,022
O6 0,015 0,013 0,05 0,025 0,029 0 0,067 0,029 0,029 0,018 0,013
O7 0,013 0,011 0,033 0,04 0,018 0,067 0 0,05 0,05 0,013 0,015
O8 0,012 0,011 0,026 0,05 0,014 0,029 0,05 0 0,333 0,013 0,02
O9 0,011 0,011 0,025 0,05 0,014 0,029 0,05 0,333 0 0,013 0,022
O10 0,015 0,013 0,029 0,018 0,05 0,018 0,013 0,013 0,013 0 0,04
O11 0,014 0,013 0,017 0,04 0,022 0,013 0,015 0,02 0,022 0,04 0
Here, сkj =1/Lkj is taken as the price, and thereLkj is the length of the cable network between the machines.
The task is to distribute data between automated workstations (groups of operators). First of all we solve this prob-
lem of distribution, using only the frequency matrix F. According to the algorithm proposed in item 1 we obtain the
following distribution:
D1 refers to the database operator O1, because 175 is the maximum for row D1 and is in column O1;
D2 refers to the database of the operator O2;
D3 refers to the database operator O11;
D4 refers to the database of the operator O2;
D5 refers to the database of the operator O8.
In the general case, using the algorithm proposed in item 2, we find the loss matrix
Теоретичні і методологічні основи програмування
D3 27 29 19 10 3 2 20 15 19 4 30
D4 56 59 43 6 18 4 27 37 30 12 30
D5 2 11 19 0 2 3 11 20 11 8 18
The matrix of values С = ║сkj║ (k = 1, 2, ..., N; j = 1, 2, ..., N) communication of the operator k with the
"outlying" database j (ckk = 0) is given in Table 3:
Table 3
Matrix С of values of operator communication
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
O1 0 0,067 0,04 0,013 0,018 0,015 0,013 0,012 0,011 0,015 0,014
O2 0,067 0 0,025 0,011 0,017 0,013 0,011 0,011 0,011 0,013 0,013
O3 0,04 0,025 0 0,02 0,067 0,05 0,033 0,026 0,025 0,029 0,017
O4 0,013 0,011 0,02 0 0,022 0,025 0,04 0,05 0,05 0,018 0,04
O5 0,018 0,017 0,067 0,022 0 0,029 0,018 0,014 0,014 0,05 0,022
O6 0,015 0,013 0,05 0,025 0,029 0 0,067 0,029 0,029 0,018 0,013
O7 0,013 0,011 0,033 0,04 0,018 0,067 0 0,05 0,05 0,013 0,015
O8 0,012 0,011 0,026 0,05 0,014 0,029 0,05 0 0,333 0,013 0,02
O9 0,011 0,011 0,025 0,05 0,014 0,029 0,05 0,333 0 0,013 0,022
O10 0,015 0,013 0,029 0,018 0,05 0,018 0,013 0,013 0,013 0 0,04
O11 0,014 0,013 0,017 0,04 0,022 0,013 0,015 0,02 0,022 0,04 0
Here, сkj =1/Lkj is taken as the price, and thereLkj is the length of the cable network between the machines. The
task is to distribute data between automated workstations (groups of operators). First of all we solve this problem of
distribution, using only the frequency matrix F. According to the algorithm proposed in item 1 we obtain the following
distribution:
D1 refers to the database operator O1, because 175 is the maximum for row D1 and is in column O1;
D2 refers to the database of the operator O2;
D3 refers to the database operator O11;
D4 refers to the database of the operator O2;
D5 refers to the database of the operator O8.
In the general case, using the algorithm proposed in item 2, we find the loss matrix
kjikij cfs = :
Table 4
Matrix S of values of loss
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
D1 19,64 20,74 25,74 26,9 22,92 25,89 21,6 43,36 55,86 18,39 15,6
D2 20,09 17,33 26,71 18,94 22,59 22,09 25,27 37,44 42,32 17,13 18,72
D3 4,055 3,505 4,456 4,912 4,236 4,841 4,118 9,72 8,428 3,633 2,772
D4 7,87 6,817 8,702 8,529 7,831 8,746 7,641 15,21 17,56 6,412 5,299
D5 2,464 1,488 2,335 3,496 3,015 3,18 2,952 5,441 8,458 2,155 1,71
Acting according to the algorithm from item 2, we obtain the following distribution (Tab. 4):
D1 refers to the database operator O11;
D2 refers to the database of the operator O10;
D3 refers to the database operator O11;
D4 refers to the database operator O11;
D5 refers to the database of the O2 operator.
The first and the second results do not match. The structure of the database is significantly influenced by the
structure of the network. The use of the proposed method of resource allocation is possible in organizations of any level.
:
Table 4. Matrix S of values of loss
O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 O11
D1 19,64 20,74 25,74 26,9 22,92 25,89 21,6 43,36 55,86 18,39 15,6
D2 20,09 17,33 26,71 18,94 22,59 22,09 25,27 37,44 42,32 17,13 18,72
D3 4,055 3,505 4,456 4,912 4,236 4,841 4,118 9,72 8,428 3,633 2,772
D4 7,87 6,817 8,702 8,529 7,831 8,746 7,641 15,21 17,56 6,412 5,299
D5 2,464 1,488 2,335 3,496 3,015 3,18 2,952 5,441 8,458 2,155 1,71
Acting according to the algorithm from item 2, we obtain the following distribution (Tab. 4):
D1 refers to the database operator O11;
D2 refers to the database of the operator O10;
D3 refers to the database operator O11;
D4 refers to the database operator O11;
D5 refers to the database of the O2 operator.
The first and the second results do not match. The structure of the database is significantly influenced by
the structure of the network. The use of the proposed method of resource allocation is possible in organizations of
any level.
Conclusion
The use of the described algorithm allows to optimally distribute the information resource in the local cor-
porative network, as well as to solve the problem of building a local network with the highest reliability and high
efficiency. A method of quasi-optimal distribution of related resources in the design of workstations in local cor-
porative network is proposed. When constructing the optimal structure of a distributed database, many factors that
influence the final decision are easily taken into account by including their impact on the cost of communication.
Література
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2012, 266p.
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References
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10. Ozsu, M.T. & Valduriez, P. (2020) Principles of Distributed Database Systems. 4th Ed.
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Received 15.08.2022
Про авторів:
1. Пурський Олег Іванович,
доктор фізико-математичних наук,
професор
Кількість публікацій в українських виданнях – 150.
Кількість зарубіжних публікацій – 69.
http://orcid.org/0000-0002-1230-0305.
2. Козлов Валерій Володимирович,
кандидат технічних наук,
доцент.
Кількість публікацій в українських виданнях – 55.
Кількість зарубіжних публікацій – 2.
https://orcid.org/0000-0002-9686-0237.
3. Томашевська Тетяна Володимирівна,
кандидат технічних наук,
доцент.
Кількість публікацій в українських виданнях – 38.
Кількість зарубіжних публікацій – 1.
https://orcid.org/0000-0001-5001-9226.
4. Дивак Володимир Валерійович,
кандидат педагогічних наук,
доцент.
Кількість публікацій в українських виданнях – 42.
Кількість зарубіжних публікацій – 1.
https://orcid.org/0000-0001-8014-815X.
31
Теоретичні і методологічні основи програмування
Місце роботи авторів:
Державний торговельно-економічний університет, 02156, м. Київ, Україна,
вулиця Кіото, 19. Тел.: (38)(044) 513-33-48
E-mail: compdep@knute.edu.ua
5. Гордійко Наталія Олександрівна,
кандидат технічних наук,
доцент.
Кількість публікацій в українських виданнях – 42.
Кількість зарубіжних публікацій – 3.
https://orcid.org/0000-0002-0925-5160.
Місце роботи автора:
Фізико-технічний інститут КПІ ім. Ігоря Сікорського, 03056, м. Київ, Україна,
проспект Перемоги, 37, корпус 1.
Тел.: (38)(044) 204-85-12
E-mail: apd.ipt@gmail.com
6. Сіницький Микола Євгенович,
кандидат фізико-математичних наук,
доцент.
Кількість публікацій в українських виданнях – 35.
Кількість зарубіжних публікацій – 2.
http://orcid.org/0000-0002-2954-615X.
Місце роботи автора:
Національна академія статистики, обліку і аудиту, 04107 м. Київ, Україна,
вулиця Підгірна, 1.
Тел.: (38)(044) 486-31-17
E-mail: kaf_it@nasoa.edu.ua
Прізвища та ініціали авторів і назва доповіді англійською мовою:
Pursky O.I., Kozlov V. V., TomashevskaT.V., Dyvak V.V.,
Hordiiko N. O., Sinitsky M.Y.
Information resources distribution between automated workstations
in local corporative networks
Прізвища та ініціали авторів і назва доповіді українською мовою:
Пурський О. І., Козлов В. В., Томашевська Т. В., Дивак В. В., Гордійко Н. О.,
Сіницький М. Є.
Розподіл інформаційних ресурсів між автоматизованими робочими
місцями в локальних корпоративних мережах
Для роботи з редактором: Пурський Олег Іванович, проф, д.ф.-м.н.,
завідувач кафедри компютерних наук та інформаційних систем ДТЕУ,
тел. 067-388-04-76, e-mail: pursky_o@ukr.net
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