INTELLIGENT APPLICATION OF DIGITAL

SHARED MANAGEMENT SYSTEM IN

JOURNAL RESOURCE INTEGRATION

Yong-Cai*

• Hunan College of Finance and Economics, Changsha, Hunan, 410000, China

•Email: 313486070@qq.com

Reception: 4 January 2024 | Acceptance: 30 January 2024 | Publication: 29 February 2024

Suggested citation:

Yong-Cai (2024). Intelligent Application of Digital Shared Management

System in Journal Resource Integration. 3C TIC. Cuadernos de desarrollo

aplicados a las TIC, 13(1), 159-177. https://doi.org/10.17993/3ctic.2024.131.159-177

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

169

ABSTRACT

In this paper, a journal resource integration system under digital sharing was designed

to assess the symmetry of the journal resource matrix using the shared resource

matrix analysis method, so that the journal resources can transmit information covertly

among the subjects. Subsequently, the collected journal resource data were

processed to improve the accuracy of information categorization and integration to

form a perfect resource management system. The nature of journal resources was

deeply understood by calculating the characteristic observable vectors of journal

resources. Finally, with the help of ant colony particle optimization algorithm, the

sharing sequence and time of journal resources were calculated, and the metadata

format of all journals was unified and standardized to complete the design of journal

resource integration system. The results show that the whole average integration

efficiency of journal resources is 92.8%. Compared with the traditional integration

system, the integration efficiency is improved by 28.3%, which fully confirms the

significant performance improvement of the designed digital system in the integration

of shared journal resources.

KEYWORDS

Digital sharing; journal resource integration; shared resource matrix; ant colony

particle; shared sequence

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

170

INDEX

ABSTRACT .....................................................................................................................2

KEYWORDS ...................................................................................................................2

1. INTRODUCTION .......................................................................................................4

2. INTELLIGENT DESIGN OF JOURNAL RESOURCE INTEGRATION SYSTEM

UNDER DIGITAL SHARING .....................................................................................6

2.1. Shared Resource Matrix Analysis ......................................................................6

2.2. Establishment of a journal resource library ........................................................7

2.3. Computing observable vectors for journal resource characterization ................8

2.4. Feature similarity determination .........................................................................8

3. SHARED MANAGEMENT SYSTEM RESOURCE CONSOLIDATION

PROGRAMME_ ........................................................................................................9

3.1. Optimizing adaptation values .............................................................................9

3.2. Optimal value solution ......................................................................................11

3.3. Journal Resource Integration Process .............................................................11

4. DIGITAL SHARING SYSTEM DESIGN ..................................................................12

4.1. General framework ...........................................................................................12

4.2. Categorized search module .............................................................................13

5. ANALYSIS OF THE EFFECT OF INTELLIGENT APPLICATION OF JOURNAL

RESOURCE INTEGRATION ..................................................................................14

5.1. Comparison of success rates ...........................................................................14

5.2. Comparison of integration efficiency ................................................................15

5.3. Integration time comparison .............................................................................16

6. CONCLUSION ........................................................................................................17

REFERENCES ..............................................................................................................17

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

171

1. INTRODUCTION

Journals are the most utilized and have the highest information value, with the

characteristics of short publication cycle, novel content, large amount of information,

and the ability to systematically reflect the latest scientific and technological

developments, which makes them essential materials for teachers and students of

colleges and universities to engage in teaching and scientific research activities [1-2].

The so-called integration of periodical resources refers to the orderly organization of

periodical resources of different types and formats based on certain needs and

requirements, and presenting them to readers in a unified way through intermediate

technology. The periodical resource system through integration, with integrated search

function, is a new type of periodical resource system with cross-platform, cross-

database and cross-content [3-4]. With the construction of digital libraries, digital

resources are becoming more and more abundant, especially journal resources [5].

There are many kinds of journal resources, large amount of data, and different forms,

and different journal resources often have independent databases, retrieval systems,

and distribution systems, which makes the journal resources relatively scattered and

cluttered, and causes a lot of inconvenience for readers to retrieve and utilize them

[6]. Therefore, intelligent integration of journal resources has become an important

part of the construction of digital digital sharing management system, which will

improve the effective utilization of journal resources by readers.

Ahmad, N identified three categories of critical success factors (CSFs), namely,

program design and implementation, quality culture and excellence, and institutional

infrastructure and support, for a total of 11 CSFs, through the observation of several

programs of King Khalid University in the process of ABET accreditation. Using the

fuzzy hierarchical analysis and the method of perfect agreement, the SFs and their

dimensions in terms of continuous academic quality assurance, ABET accreditation

were Relative importance was ranked comprehensively [7]. Rini, G. P et al. explored

the intrinsic link between customer orientation in terms of unique resource integration,

exchange memory system. Identify the key factors affecting customer service

performance based on the theory of competitive advantage of firms. Using Indonesian

hotel managers and administrators as research subjects, 327 valid questionnaires

were distributed with a validity rate of 70.6%. It was found that firm-specific resource

integration enhances customer service quality, with customer-oriented antecedents of

customer orientation and interactive memory [8]. Eppard, J et al. tested the flipped

learning approach in a class for one semester, triangulated by exam scores of the

students, interviews with participants, and instructor's reflections. The results indicated

that flipped learning had positive outcomes in terms of increasing student self-efficacy,

promoting leaner independent learning, and providing resources for concept

introduction and review [9]. Wunderlich, J et al. suggest that there is still a gap in how

to select appropriate assemblies for different purposes and a need for guidance for

practitioners. An architecture is proposed with three components to guide the

integration effort, designing a four-stage integration evaluation methodology and

classifying integration based on qualitative explorations into three categories, and

finally proposing an incremental approach to selecting the appropriate aggregation

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

172

approach based on the needs of the evaluation [10]. Bae, J et al. proposed a

reinforcement learning based network scheduling algorithm for single-hop downlink

scenarios, whereby the network optimization problem is formalized as a Markov

decision process problem by introducing a new state-action value function. And a

reinforcement learning algorithm for upper confidence bound exploration is designed

to ensure that the loss of performance is minimized [11]. In modern cities,

transportation digitization plays a crucial role as an important means of connecting all

kinds of transportation service providers and users. Jabbari, M et al. based on the

existing pedestrian road network model. The digital modeling took urban residents as

the main body and constructed a multi-level pedestrian system with the help of digital

technology [12]. Rajabi, E et al. made e-learning content public on the Internet, which

is conducive to achieving the sharing and reuse of teaching resources, and enhances

the interoperability of data in the network. Utilizing the principle of Linked Data,

educational metadata is made public as Linked Open Data and its content is

interconnected with web datasets in order to improve the interoperability of web

resources and thus achieve the sharing and reuse of educational resources [13]. Nijs,

M et al. proposed that the online educational repository is a good global resource for

science and technology and that the global virtual teaching and online lectures can

provide the audience around the world with evidence-based evidence-based

knowledge from which knowledge including distance learning can be gained. The

library is an excellent resource tool for conducting resource sharing workshops for

ART professionals and providing effective hands-on vocational education training for

ART professionals through virtual training tools, thus reducing unnecessary

duplication of efforts [14].

In this paper, first of all, through the shared resource matrix analysis method, the

system carries out in-depth analysis of periodical resources, determines the symmetry

of the matrix to the periodical resources, and realizes the hidden transmission of

information between the subjects. A complete journal resource library is established,

and a multi-level intelligent walking system is constructed through fine processing and

classification and integration. The system further calculates the observable vectors of

journal resource features to deeply understand the nature of journal resources.

Through the determination of feature similarity, it provides the basis of intelligent

decision-making for the subsequent resource integration. In the resource integration

scheme of the shared management system, the system optimizes the adaptation

values and solves the optimal values through intelligent algorithms to ensure that the

diversity of resources and user needs are fully considered in the integration process.

An intelligent journal resource integration process is designed to make the system

more efficient and intelligent in the integration process. Finally, the design of the digital

sharing system adopts an intelligent classification and search module, which enables

users to obtain the required journal resources more conveniently and intelligently. The

construction of the overall framework ensures that the structure of the system is

reasonable and stable.

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

173

2. INTELLIGENT DESIGN OF JOURNAL RESOURCE

INTEGRATION SYSTEM UNDER DIGITAL SHARING

2.1. SHARED RESOURCE MATRIX ANALYSIS

Shared Resource Matrix Analysis was first proposed by R. Kemmerer in 1982, the

algorithm stores the trusted computing base of the system in the form of a matrix,

where the original language has access rights to the trusted computing base variables

[15-16]. Under the operation of the system, the storage is the read and write

permissions that the system calls have on the shared resource variables. When using

the shared resource matrix analysis method, it is first necessary to check all shared

resources that can be accessed by the subjects, and then to determine whether they

are able to pass information covertly between all subjects. In implementing this step,

each of the original languages in the system needs to be carefully examined.

Given a matrix of size , it needs to be satisfied if is

symmetric about journal resource :

(1)

In the formula, indicates the number of journal resources less than

in statistical array , and indicates the number of journal resources

greater than in statistical array . Through the position of journal resources in the

matrix, the symmetry of the matrix about the journal resources can be judged, if the

matrix is symmetrical about the standard symmetry of the journal resources, formula

(1) must be established, and the value of formula (1) also reflects the symmetry of the

matrix about the journal resources.

Using matrix analysis to analyze the content of journal resources and resource

search records, user interests can be fully explored so that resource

recommendations can be given to users. The one-dimensional matrix is generalized

to a high-dimensional space of size , where is the sample size and is the

dimension. Then the space can be described by a matrix of size as:

(2)

A=[e1,e2,e3,L,em]

1×m

Less (A,ei)−More (A,ei)= 0

Less (A,ei)

More (A,ei)

n×m

Mar

n×m

ar =

L a

a21 a22 L a2jL a2m

M M O M O M

an1ai2L aij L aim

M M O M O M

an1an2L anj L anm

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

174

A point in a known space is symmetric about the projective

position of in every dimension if the space is symmetric about , i.e., the following

condition is satisfied:

(3)

where denotes the rd column of matrix , where denotes the th

dimension of . In order to better represent the symmetry of space about , the

concept of symmetry rate is designed as:

(4)

The symmetry ratio reflects the symmetry of the matrix with respect to the points;

the higher the ratio, the more likely it is that the matrix is symmetric with respect to .

2.2. ESTABLISHMENT OF A JOURNAL RESOURCE LIBRARY

In order to improve the accuracy of information classification and integration, the

collected journal resource data are processed. Setting the resource training set as ,

which contains kinds of resources, through this setting, we can get the expected

value of the classification result of the resource kinds, which can be expressed by the

formula as follows:

(5)

Where is the information in the journal resource, and is the probability of

data classification rationality. Connecting the repository and the terminal to form a

complete resource management system to provide users and managers with more

convenient and efficient journal resource services.

xi=[xi1,xi2,L,xim]

Less

(

Mar1,xi1

)

−More

(

Mar1,xi1

)

= 0

Less (Mar2,xi2)−More (Mar2,xi2)= 0

Less (Mar3,xi3)−More (Mar3,xi3)= 0

Less (Marm,xim)−More (Marm,xim)=

Marm

Mar

xi =

∑

j=1

n−Less

(

Marj,x−

)

−More

(

Marj,xij

)

In fo(A)=−

∑

i=1

plg p

In fo

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

175

2.3. COMPUTING OBSERVABLE VECTORS FOR JOURNAL

RESOURCE CHARACTERIZATION

First, the total number of characteristic variables of the initial sample is determined

and denoted by . Second, by orthogonally transforming the characteristic variables

of the initial sample to obtain integrated variables and

constructing a coefficient matrix , the resource integration characteristic equation is

expressed as:

(6)

The non-negative eigenvalues of sample are sorted to meet the requirements of

. The top resource integration features can be obtained through

equation (6) with the following expression:

(7)

where is the uncertainty of journal resource integration and is the journal

resource integration coefficient.

Using to describe the first journal resource features as the ratio of all first-order

features is expressed as:

(8)

where is the information of each type of feature and is the observed

variable. The observable vector of resource features is calculated according to Eq. (8)

and the expression is:

(9)

Where is the unobservable vector, is the factor loadings of journal resource

integration features, and is the factor affecting . By calculating the journal resource

characteristic observable vector, it is possible to understand the journal resources

more deeply.

2.4. FEATURE SIMILARITY DETERMINATION

In the process of journal resource integration, vectors are utilized to complete the

representation of journal text information. The processed journal text information is

X=x1,x2,⋯,xn

(y1,y2,y3)

(i) =

|R⊗X|

{(

3)}

⊗

,⋯,x

λi

λ1≥λ2≥λn≥0

(p) =

m⊗λ(i)

ξ(e)

⊗η(r

)

η(r)

ξ(e)

m⊗β(p)

μ(R)

×v(e)(σ*⊗κ

)

v(e)

∂

(X) =

X⊗F

(aij)

n∓m

⊗ci⊗εi×X

εi

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

176

represented by vectors, firstly, the feature words in the journal text are extracted,

assuming that the extracted feature word is , then the journal text

information of the metadata item can be represented as:

(10)

Where is the feature term of the nd text, and is the weight occupied by this

feature term.

It is calculated according to the similarity algorithm of cosine:

(11)

Where is the metadata item of journal resource information , and is the

weight occupied by the th journal text information feature item.

According to the above calculation, the semantic similarity of journal resources can

be derived as follows:

(12)

Where denotes the weighted weights of the metadata items.

When the calculated similarity is 1, the resources represented by the two calculated

sets of metadata are proved to be equivalent, and if the calculated similarity is

between , the two sets of journal resources are proved to be similar, where

denotes an empirical value and is usually considered to be .

3. SHARED MANAGEMENT SYSTEM RESOURCE

CONSOLIDATION PROGRAMME_

3.1. OPTIMIZING ADAPTATION VALUES

Using ant colony particle swarm optimization algorithm, according to the time given

to the click frequency of journal resource categories and resource categories,

respectively, click on the path of the 2 indicators of different weights, calculate the

similarity of the user, as a way to improve the efficiency of pre-school periodical

resource integration, to provide core support for the intelligent design of the journal

resource integration system under the digital sharing [17-19].

Let there be

collection of journal resource tasks in the system, i.e.,

, which needs to be implemented on the collection of resource

nodes , where the computational resource node is described by

m1,m2,⋯,mx

dm=(m1,q1;m2,q2;⋯;mx,qx)

cos

(

x1,x2

)

×q′

+⋯+q

×q′

∑xq2

x×∑xq′ 2

q′

cos (x1,x2)=qdm cosdm (x1,x2)

qdm

α∼1

0.2 < α< 1.0

T={T1,T2, LTn}

G={G1,G2,LGm}

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

177

and the standalone task is described by . A

feasible sequence of journal resource assignments is described by particle positions,

and if the journal resource tasks are realized on the resource nodes, the position of

particle is shown below:

(13)

where denotes a feasible journal resource organization solution.

Set the time used by the journal resource task to achieve resource consolidation as

, the total elapsed time to perform the journal

resource subtasks as , and set to be extremely small as the optimization

objective. Then the adaptation value for one organizing is:

(14)

In the ant colony algorithm, the ant pheromone concentration is proportional to the

selection probability. The click frequency of each resource category can be obtained

by calculating the number of clicks on each resource category and the resources

under the resource category visited by the user in each session, and quoting it with

the total number of clicks on all the resources in this session. Then take the average

value of the click frequency of the resource category in a certain period of time,

construct the click frequency user matrix of the resource category, and find the

similarity of the click frequency vector of the user's resource category by solving the

similarity of the click frequency vector of the user's resource category in the frequency

user matrix of the resource category. Initially each path has the same pheromone, in

moment nodes, the ant is described by , then the probability that the ant picks the

next node is:

(15)

Where, denotes the relative weight of the predicted value of computational power,

denotes the quality of the line of the ant at the node,

denotes the node of computational resources that the

ant did not choose, denotes the relative weight of pheromone, denotes the

forbidden table of the ant, denotes the relative weight of the quality of the line,

denotes the strength of pheromone of the ant to view the node at the th

moment, from the th node, and is a constant, and is a random coefficient.

Gi= (i∈[1,m])

Ti= (i∉[1,n])

Xk=

{

x1,x2,L,xj,L,xn

}

Gij = (i∈{1,2,L,m}, j∈{1,2,L,n})

Gma x

Gma x =

max

{Ci}

ij =

(

Tij(d)

)α

Lqij(d)β

(

EV α

j(d)γ

)

Lqij(d)β

∑n∈e(k)

(Tij(d))α

Lqij(d)βg21

(EV α

n(d)γ)

Lqin(d)β

u(k) = {0,1, L, n−1} −e(k)

e(k)

Tij(d)

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

178

3.2. OPTIMAL VALUE SOLUTION

In order to solve the global optimal solution and ensure that the line quality or

pheromone value of the node is extremely high, this paper controls the probability of

the ants selecting a node by using random coefficients and constants assigning a

forgetting factor to each access path to the journal resource category by the user in a

certain period of time [20]. For nodes whose pheromone concentration is not 0, the

solution is:

(16)

At moment , the ant realizes a 1-times loop with an update rule for the amount of

information on each path:

(17)

(18)

Where, denotes the persistence of the ant trajectory, is the informative

increment of link , denotes the number of pheromones left by ant on link

within this cycle, and denotes the length of the link that ant traverses for a

week, the informative increment of link is updated by Eq:

(19)

Set the number of iterations to 0, i.e. , set virtual nodes

have ants, in order to solve the node, need to initially organize the sequence,

through the taboo table of ants to remove the resource node labeling, a calculation of

the resource node , which can be achieved through the formula (18) to select. Within

the resource organizing sequence, set the position of the node, based on the taboo

table of ants to remove . The completion time of the resource organizing sequence,

according to the formula (17) to solve the optimal solution, to get the completion of the

optimal periodicals resource organizing sequence with time [21].

3.3. JOURNAL RESOURCE INTEGRATION PROCESS

The main idea of the integration of journal resources is to unify and standardize the

operation of all journal metadata formats, and then add all these metadata to the

established journal database. The metadata of all journal databases to be imported

are described in a unified Extensible Markup Language (XML) format, so as to lay a

good foundation for future data exchange and dumping. Then these journal resource

metadata described in XML format will be imported into the journal database, the

Tij(t+ 1) = ρ×Tij(t)

ij(t+n)=ρ

(

Tij(t)+1

)

ΔTi

Tij =

∑

k=1

ΔT

ΔTij

(i,j)

Tif =

Lk,

N= 0,u(k) = {1,2,L,n}

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

179

specific implementation process is shown in Figure 1. In the XML format metadata

mapping to relational databases, the use of structural mapping approach, that is, in

the storage of relational databases, the first according to the schema or mining out the

schema information inherent in the document to generate the corresponding relational

schema, and then according to the generated relational schema of the XML document

to analyze the decomposition and will be stored in the corresponding data table.

Journal resource data may come from different databases, they all have their own

different complex formats, due to the customizability and extensibility, solves the

problem of a unified interface for data, enough to express various types of data.

Figure 1. Journal resource integration realization process

4. DIGITAL SHARING SYSTEM DESIGN

4.1. GENERAL FRAMEWORK

The overall structure design of the system is to divide the system into logical

structures on the basis of the demand analysis, to determine the distribution of

functions among the levels and parts within the levels, and to determine the

interrelationships among the levels and parts. The system can be divided into three

layers, i.e., data layer, business processing layer and application layer. After the

system is separated by layers, the change of functions can be realized by changing

the related layers, and due to the relative independence of each layer, the change of a

certain layer will not have an impact on other layers. The advantage of dividing the

system into so many levels is that it can make the system architecture more clear, so

that the function accomplished by each level is relatively single, and the code of the

function is regular, which makes it possible to put more energy into the processing of

business logic. The overall framework of the system is shown in Figure 2. The unified

database retrieval module is an effective way to integrate journal resources, due to

the rich content of journal resources with diverse carriers, for which a retrieval module

should be set up to integrate the journal resource databases into one, so that the

readers can conveniently retrieve the required documents across the databases

through the unified retrieval interface. In the classification search module, it mainly

provides users with three ways of searching by database, searching by journal and

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

180

searching by category [22]. These three ways also provide first letter navigation, and

also provide a secondary search function, that is, on the basis of the first search

results and then further search.

Figure 2. Overall system framework diagram

4.2. CATEGORIZED SEARCH MODULE

The whole structure of the classification search module is shown in Figure 3. The

application of digital sharing management technology in the integration of journal

resources mainly lies in realizing the rapid query and sharing of journal resources, and

the reasonable management of resources is particularly important due to its huge data

and information base. Digital sharing management technology is used to organize idle

resources and solve communication and computing needs, thus saving a large

number of duplicated investments and improving the utilization rate of resources.

Figure 3. Structure of the classification search module

Functional architecture design as shown in Figure 4, the system is divided into

digital resource submission function module, resource audit management function

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

181

module, resource use function module and system management function module 4

parts. However, due to the system startup, the database needs to be initialized and

set up, after entering the user name and the correct password, the journal resource

integration system can be started to ensure that the management of system files is

safe This part needs to be set up using a computer program. After completing the

above operations, the integration of journal resources is realized.

Figure 4. Functional architecture design

5. ANALYSIS OF THE EFFECT OF INTELLIGENT

APPLICATION OF JOURNAL RESOURCE

INTEGRATION

5.1. COMPARISON OF SUCCESS RATES

From a university library, 120,000 shared digital journal resources are randomly

selected for integration, detecting the loss rate and misdetection rate of the resources

after the integration of this paper's system with the traditional integration system and

the Hadoop-based digital resource integration system for digital journal resources in

this university library, and comparing the integration success rate of each system with

the performance of the system. Table 1 shows the results of the loss rate and false

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

182

detection rate of the three systems, and with the increase of the number of resources,

the performance of the system in this paper is still able to remain stable. For example,

at 120,000 resources integration, the loss rate is only 0.31% and the misdetection rate

is 0.10%, reflecting the excellent performance of the system in large-scale digital

journal resources integration. In contrast, the traditional integration system shows a

higher loss rate of 2.43% for the complete 120,000 shared digital journal resources,

while the Hadoop-based digital resource integration system still lags behind this

paper's system in terms of the integration success rate, with a loss rate of around

1.45%, although it incorporates Hadoop technology. It shows that the system in this

paper improves 2.12% in terms of loss rate compared with the traditional integration

system, and leads the Hadoop-based system in terms of integration success rate. The

system in this paper performs well when the number of resources is less than 80,000,

which further confirms its efficiency in small-scale resource integration scenarios. It is

possible to confirm that the journal resource integration system designed in this paper

under digital sharing shows more excellent performance in terms of integration

success rate, loss rate and false detection rate, which validates the superiority of the

system.

Table 1. Comparison of loss rate and false detection rate results of three systems

5.2. COMPARISON OF INTEGRATION EFFICIENCY

In order to further analyze the reliability of the integration system, it can be

analyzed by comparing the integration efficiency of journal resources, the higher the

integration efficiency of journal resources, the better the integration stability of its

system, and the integration efficiency of the traditional integration system and the

Hadoop-based digital resource integration system. Figure 5 shows the test results of

integration efficiency, the average integration efficiency of traditional integration

system is 64.5%, and the highest efficiency is only 66.8%. In contrast, the average

integration efficiency of the Hadoop-based digital resource integration system is

78.6%, and the highest efficiency is 83.4%, showing an improvement of 14.1% after

Number of

resources /

Article

This paper integrates

the system

Traditional integration

system

Hadoop-based

integration system

Attrition

rate /%

False

Detection

Rate /%

Attrition rate /

False

Detection

Rate /%

Attrition

rate /%

False

Detection

Rate /%

20000 0 0 0.38 0.31 0.27 0.21

40000 0 0 0.79 0.41 0.46 0.29

60000 0 0 1.21 0.56 0.78 0.37

80000 0.12 0 1.56 0.95 0.95 0.46

100000 0.20 0.09 1.89 1.47 1.23 0.53

120000 0.31 0.10 2.43 2.38 1.45 0.79

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

183

integrating Hadoop technology. Despite the improvement in system performance, it

still falls short of practical application and requires further improvement. In contrast,

the journal resource integration system designed in this paper under digital sharing

shows higher integration efficiency when the number of iterations gradually increases.

The integration efficiency of journal resources is 91.8%, 92.9%, 92.2%, 93.3%, 93.8%,

and the average integration efficiency is 92.8%. Compared with the traditional

integration system, the system in this paper has improved 28.3% in terms of

integration efficiency, which provides a strong support for the excellent performance of

the system. It is clearly demonstrated that the designed system for integrating journal

resources under digital sharing has significant advantages in terms of the reliability of

the integration of shared journal resources.

Figure 5. Comparative results of journal resource integration efficiency

5.3. INTEGRATION TIME COMPARISON

Finally, the system in this paper and the traditional integration system, Hadoop-

based digital resource integration system for university library journal resource

integration time, and record the results of the analysis and comparison, has been

verified that the system integrates the efficient performance of the journal resources,

the comparison is shown in Figure 6. In this paper, the digital sharing of journal

resources integration system in the integration of digital journal resources in college

libraries, the time used is the shortest, and with the growth of the number of

resources, the overall rise in the time used is relatively slow, and there is no

phenomenon of time-consuming due to the growth of the number of resources is too

long. After the number of journal resources increased in turn, the integration system in

this paper took 49ms, 55ms, 79ms, 82ms, 96ms, 120ms, the overall time is shorter,

and in the case of the highest number of resources, the time is also in the 150ms

below, stable performance. While the other two systems have faster growth in the time

used when the number of resources is higher, the time used by the traditional

integration system is 362ms, 401ms, 495ms, 517ms, 844ms, and 909ms, and the

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

184

time used by the Hadoop-based digital resource integration system is 300ms, 398ms,

469ms, 503ms, 790ms, and 811ms, respectively, and the time used by the Hadoop-

based digital resource integration system is 300ms, 398ms, 469ms, 503ms, 790ms,

and 811ms, respectively, and the performance is stable. Hadoop's digital resource

integration system is not excellent although its effect is refined. It indicates that the

performance of the other two systems is not stable, which further confirms the

excellent performance of the system in this paper in the field of digital journal resource

integration, and provides strong support for the efficient operation of the system.

Figure 6. Comparison of time spent on integration of different systems

6. CONCLUSION

In this paper, we design the journal resource integration system under digital

sharing, unify and standardize the operation of all journal metadata formats, and then

add all these metadata into the established journal database to complete the design of

the journal resource integration system under digital sharing. The performance of this

paper's system is verified through the comparison of success rate, integration

efficiency, and integration time, and the results show that the system performance of

this paper's system remains stable with the increasing number of resources, and the

loss rate is only 0.31% at 12,0000,. The average integration efficiency of this paper's

system is 92.8%, which is 28.3% higher than the traditional integration system, and

the overall time is shorter, which is below 150ms in the case of the highest number of

resources. Therefore, it shows that this paper proposes a journal resource integration

system under digital sharing, with high integration efficiency and stable performance,

and the intelligent simulation application is of practical significance.

REFERENCES

(1) Danilina, V., & Grigoriev, A. (2020). Information provision in environmental policy design.

J. Environ. Inform, 36, 1-10.

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

185

(2) Huang, M., Fan, X., Jian, H., Zhang, H., Guo, L., & Di, L. (2022). Bibliometric Analysis of

OGC Specifications between 1994 and 2020 Based on Web of Science (WoS). ISPRS

International Journal of Geo-Information, 11(4), 251.

(3) Yaniawati, P., Kariadinata, R., Sari, N., Pramiarsih, E., & Mariani, M. (2020). Integration

of e-learning for mathematics on resource-based learning: Increasing mathematical

creative thinking and self-confidence. International Journal of Emerging Technologies in

Learning (iJET), 15(6), 60-78.

(4) Tzenios, N. (2020). Examining the Impact of EdTech Integration on Academic

Performance Using Random Forest Regression. ResearchBerg Review of Science and

Technology, 3(1), 94-106.

(5) Zhang, W., Liu, B., & Tsai, S. B. (2022). Analysis and research on digital reading platform

of multimedia library by big data computing in internet era. Wireless Communications

and Mobile Computing, 2022, 1-10.

(6) He, Y. (2022). Design of online and offline integration teaching system for body sense

dance based on cloud computing. Journal of Interconnection Networks, 22(Supp05),

2147001.

(7) Ahmad, N., & Qahmash, A. (2020). Implementing Fuzzy AHP and FUCOM to evaluate

critical success factors for sustained academic quality assurance and ABET

accreditation. PloS one, 15(9), e0239140.

(8) Rini, G. P., & Kusumawardhani, A. (2023). Orchestrating firm-specific resource

integration to achieve customer service performance: an investigation in the hotel

context. International Journal of Innovation Science.

(9) Eppard, J., Rodjan-Helder, M. G. D., Baroudi, S., & Reddy, P. (2021). Integrating flipped

learning into an English pre-sessional class at a public university in the UAE: reports

from an SLL university classroom. International Journal of Virtual and Personal Learning

Environments (IJVPLE), 1(2), 65-86.

(10) Wunderlich, J., Armstrong, K., Buchner, G. A., Styring, P., & Schomäcker, R. (2021).

Integration of techno-economic and life cycle assessment: Defining and applying

integration types for chemical technology development. Journal of Cleaner Production,

287, 125021.

(11) Bae, J., Lee, J., & Chong, S. (2021). Learning to schedule network resources throughput

and delay optimally using Q+-learning. IEEE/ACM Transactions on Networking, 29(2),

750-763.

(12) Jabbari, M., Ahmadi, Z., & Ramos, R. (2022). Defining a digital system for the pedestrian

network as a conceptual implementation framework. Sustainability, 14(5), 2528.

(13) Rajabi, E., Sanchez-Alonso, S., Sicilia, M. A., & Manouselis, N. (2017). A linked and open

dataset from a network of learning repositories on organic agriculture. British journal of

educational technology, 48(1), 71-82.

(14) Nijs, M., Morroll, D., Lynch, C., Levett, S., Fleming, S., Chin, R., ... & Erreb. Agerholm, I.

(2021). P–786 Virtual continual professional education programs in ART in time of

SARS-CoV–2: do they deliver?. Human Reproduction, 36(Supplement_1), deab130-785.

(15) Tian, Z., Yang, S., & Zhang, C. (2023, August). Accelerating Sparse General Matrix-

Matrix Multiplication for NVIDIA Volta GPU and Hygon DCU. In Proceedings of the 32nd

International Symposium on High-Performance Parallel and Distributed Computing (pp.

329-330).

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

186

(16) Sky, A., Polindara, C., Muench, I., & Birk, C. (2023). A flexible sparse matrix data format

and parallel algorithms for the assembly of finite element matrices on shared memory

systems. Parallel Computing, 117, 103039.

(17) Sun, Z. Analysis of the Optimization Strategy Based on the Cooperation of Drones and

Intelligent Robots for the Inspection of Substations. Academic Journal of Engineering

and Technology Science, 5(9), 44-50.

(18) Berri, J., Benlamri, R., Atif, Y., & Khallouki, H. (2021). Web Hypermedia Resources

Reuse and Integration for On-Demand M-Learning. International Journal of Computer

Science and Network Security, 21(1), 125-136.

(19) Kanso, B., Kansou, A., & Yassine, A. (2021). Open capacitated ARC routing problem by

hybridized ant colony algorithm. RAIRO-Operations Research, 55(2), 639-652.

(20) Garzón Artacho, E., Martínez, T. S., Ortega Martin, J. L., Marin Marin, J. A., & Gomez

Garcia, G. (2020). Teacher training in lifelong learning—The importance of digital

competence in the encouragement of teaching innovation. Sustainability, 12(7), 2852.

(21) Davis, J. J., Wattam, A. R., Aziz, R. K., Brettin, T., Butler, R., Butler, R. M., ... & Stevens,

R. (2020). The PATRIC Bioinformatics Resource Center: expanding data and analysis

capabilities. Nucleic acids research, 48(D1), D606-D612.

(22) Shen, Y. B., & Gadekallu, T. R. (2022). Resource search method of mobile intelligent

education system based on distributed hash table. Mobile networks and applications,

27(3), 1199-1208.

https://doi.org/10.17993/3ctic.2024.131.159-177

3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529

Ed.44 | Iss.13 | N.1 January - March 2024

187