Data Mining, Business Intelligence, Grid and Utility Computing:

A Bibliometric Review of the Literature from 2015 to 2020

Ernani Damasceno

1,2,3 a

, Ana Azevedo

and Manuel Pérez-Cota

Department of Computing, Federal Institute of Triangulo Mineiro, Campus Paracatu, Brazil

CEOS.PP, ISCAP, Polytechnic of Porto, Porto, Portugal

Higher School of Computer Science, Vigo University, Vigo, Spain

Keywords: Bibliometric Review, Data Mining, Business Intelligence, Grid and Utility Computing, Systematic Review,

Essential Science Indicators.

Abstract: Bibliometric review is a type of systematic review used to analyze a wide range of articles or scientific

publications using statistical tools to identify trends in many articles. However, there are some areas of study

that are more consolidated with a wide range of studies, such as Data Mining (DM) and Business Intelligence

(BI). New tools are being researched to provide a more effective way of using technologies in organizations,

namely, Grid Computing (GC) and Utility Computing (UC). Thus, this article aims at showing analysis of

publications databases in order to verify whether there are studies on DM and BI together with GC and UC

from 2015 to 2020. The purpose is to demonstrate not only the quantity but also some aspects, such as relations

between topics, number of publications per year, main countries and institutions, research network and H5

index from Google Scholar. Finally, the results are shown through the number of publications, percentages,

and the most relevant subjects, based on Essential Science Indicators based on Essential Science Indicators,

which determine the influences of countries, institutes, and authors in a specific field of study.

1 INTRODUCTION

It is essential that researchers become increasingly

critical and try to understand the things that are

requested, noting why they are requested. We are

going through a moment of transition in which new

international requirements are imposed so that

science can be developed correctly (Liao, Tang, Li, &

Lev, 2018). Essential Science Indicators (ESI) are

analytical databases built based on the academic

literature and their references. Accordingly,

researchers can understand the development and

influence of some noted scientists, research

institutions, countries (or regions) and academic

journals in a certain field. It is known that the amount

of information is large and, as new studies appear

every day, scientific articles are used to support new

research. Hence, there are concerns regarding where

the researcher brings information. It is possible to

filter the scientific information through a systematic

https://orcid.org/0000-0002-7298-4681

https://orcid.org/0000-0003-0882-3426

https://orcid.org/0000-0003-0471-6981

search. The systematic search seeks to understand

what the search process was, that is, what steps were

used to find the information. Bibliometric

performance analysis evaluates the impact of

scientific output based on publications and their

citations, while science mapping illustrates the

intellectual structure of the journal and the evolution

of the main research themes (López-Robles, Otegi-

Olaso, Arcos, Gamboa-Rosales, & Gamboa-Rosales,

2018). However, when the subject is selected, it is

important to highlight how the national and

international literature are approaching a specific

subject, for example, who are the researchers, what

are the titles, what are the journals, if there is any

predominant journal for a particular subject, if there

is any country that is more dedicated to the subject.

Considering these aspects, Bibliometric Analysis has

been used in various fields. The application of

bibliometric techniques has become much simpler as

online databases with bibliographic data have

Damasceno, E., Azevedo, A. and Pérez-Cota, M.

Data Mining, Business Intelligence, Grid and Utility Computing: A Bibliometric Review of the Literature from 2015 to 2020.

DOI: 10.5220/0010527303670373

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 367-373

ISBN: 978-989-758-509-8

367

emerged, and software has been developed to support

the handling of bibliometric data and the visual

representation of bibliometric networks (Walsh &

Renaud, 2017). Over the years, an increasing number

of indicators and tools have been developed to

quantify the research performance and contributions

of authors, journals, institutions, and countries

(Kaffash, Nguyen, & Zhu, 2020).

This study aims to determine the evolution of

publications from 2015 to 2020, see the most

productive authors and the most relevant research

networks in the fields presented. More recent

publications were included because they are

considered more relevant. Additionally, the aspects

considered are influential institutions, journals,

countries, and areas. It is intended to verify that there

are few studies related to concepts already widely

used, such as Data Mining (DM) and Business

Intelligence (BI), in conjunction with new ones,

namely, Grid Computing (GC) and Utility

Computing (UC). As a result, provide guidance

regarding the number of studies that are being carried

out in the area to help future researchers to see how

much it is necessary to improve studies in these fields.

The remainder of this paper is organized as follows.

Section 2 presents the background describing the

main characteristics of DM, BI, GC, UC, and related

works. Section 3 presents the methods used in this

study. Section 4 presents the results of the study.

Finally, Section 5 concludes the paper.

2 BACKGROUND

In this section, a brief introduction to DM, BI, GC,

and UC is presented. Following that, concepts and

main characteristics are explained in order to

understand the general definitions of each subject and

the relation between them in the organizational

context. However, only recent publications were

listed because the objective of this paper is to present

the current scenario of the concepts.

2.1 Data Mining (DM)

The concept of DM is already immensely popular

around the world in different areas, such as business

activities and the realm of commerce. To avoid

misconceptions or misunderstandings, it is important

to explain what DM is about. DM is a technique that

allows patterns or models to be obtained from the

gathered data. This technique is applied in various

environments, such as in biological fields,

educational and financial applications, industry,

police, and political processes (Viloria, et al., 2019).

In addition, DM is the incorporation of quantitative

methods called mathematical methods, which may

include mathematical equations, algorithms, some

prominent methodologies of traditional logistic

regression, neural networks, classification, and

clustering. Endorsing DM is intended to provide real

solutions for decision makers to develop their

businesses (Rahim, et al., 2018).

2.2 Data Mining and Business

Intelligence

In today's modern business world, companies

generate a large amount of data. Because of this large

amount of data, it is difficult to obtain a global view

of how the company is doing in all its activities

without having to look at several reports in different

locations. What organizations really need to do is

bring these disparate data sources together and

analyze them together to get a clearer picture. Thus,

BI acts as a strategic factor for a company or

organization, generating a competitive advantage that

provides privileged information to respond to

business problems (Monsalve, Carreño, Gutiérrez,

Molina, & Rangel, 2019). In the past, everything was

stored in spreadsheets and local databases. However,

in the online era, there are social media and cloud-

based business services, all of which generate large

amounts of data. Therein lies the biggest challenge for

BI. Trying to solve this problem, if DM processes are

added to BI, it is possible to create social media

mining and use it to identify influential customers in

a social networking site, detect implicit or hidden

groups in a social networking site, perceive customer

opinions related to their product or service’s

satisfaction for proactive planning, develop

recommendation systems to maintain existing

customers and gain new ones, or build and strengthen

trust among customers or between customers and

other stakeholders (Kurnia & Suharjito, 2018).

2.3 Grid and Utility Computing Tools

GC enables access to distributed heterogeneous

resources using web services. These resources can be

data sources (files, databases, web sites, etc.),

computing resources (multiprocessors,

supercomputers, clusters) and application resources

(scientific applications, information management

services, etc.) (Liu, Pacitti, Valduriez, & Mattoso,

2015). Equally, UC helps reduce initial investment.

As the computing requirements for an individual or

an organization change, the billing changes

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accordingly without incurring any additional costs. If

usage is reduced, billing will also be reduced

accordingly (Malik, Wani, & Rashid, 2018). Both GC

and UC are branches of cloud computing; therefore,

an emerging field of computer science that takes the

IT sector to a new level. In addition, cloud computing

is an advancement of various combined technologies,

such as Distributed Computing, Utility Computing,

Virtualization, to provide IT resources and services

over the Internet on pay as per use manner (Haris &

Khan, 2018).

2.4 Related Works

Bibliometric reviews analyze an extensive amount of

published research by using statistical tools to

determine trends and citations and/or co-citations of

a particular theme by year, country, author, journal,

method, theory, and research problem (Paul &

Criado, 2020). To understand these relevant aspects,

this paper presents an overview of the publications in

Google Scholar, checking the number of bibliometric

publications concerning GC and UC.

This analysis aims to demonstrate the number of

bibliometric publications found in specific subjects in

Google Scholar, such as GC and UC. Then, the

keywords used were “grid computing” +

“bibliometric” and “utility computing” +

“bibliometric” for the last five years.

Figure 1 illustrates that there are more

bibliometric articles on GC than on UC. It is also

important to see when the articles were published.

Figure 1: Number of publications by search.

Figure 2 demonstrates the number of publications

since 2015 and, as can be observed, there is not much

variation in the number of publications.

Following the analysis, Google Scholar metrics

provide an easy way for authors to quickly gauge the

visibility and influence of recent articles in scholarly

publications. The metric used in this study is the h5-

https://endnote.com/

index, which consists of the h-index for articles

published in the last five years. It is the largest

number h such that h articles published in 2015-2019

have at least h citations each. For example, a

publication with five articles cited by, respectively,

17, 9, 6, 3, and 2, has an h-index of 3.

Figure 2: Number of publications per year.

3 METHODS

To achieve the aims of this study, a bibliometric

review of the four topics, BI, DM, GC and UC, was

adopted. First, searches for publications that address

topics separately were carried out, and then searches

were conducted with articles that make relationships

between subjects. The analysis was conducted

through citations; that is, article citations were

downloaded from different academic databases, such

as Google Scholar, Web of Science, Library of

Congress, PubMed, SciELO and Scopus. In this type

of analysis, it is necessary to search for a large amount

of data; therefore, EndNote

software was used to

speed up the data collection process.

The bibliometric review method was used with

the keywords "grid computing" + "data mining",

"utility computing" + "data mining", "grid

computing" + "business intelligence" and "utility

computing" + "business intelligence” from 2015 to

2020. Explaining, the choice of these keyword

combinations is corroborated in view of the fact that

DM has proved to be a phenomenally robust tool,

which smoothens the analysis and interpretation of

large volumes of complex data. As a result, given the

complexities involved in operating and maintaining

grid environments efficiently and the ability of data

mining to analyze and interpret large volumes of data,

it is obvious that ‘mining grid data’ could be a

solution to improving the performance, operation and

maintenance of grid computing environments

Data Mining, Business Intelligence, Grid and Utility Computing: A Bibliometric Review of the Literature from 2015 to 2020

369

(Hussain, Naser, Begum, Shaik, & Shaik, 2015). Grid

enables organizations to create a managed, shared grid

computing environment for processing large volumes

of data and analytic programs (Ruzgas & Dabulytė-

Bagdonavičienė, 2017). Finally, it is understood that

the combination of these issues is important to

understand how organizations are using new tools

together with those already established. In summary,

the combination of these issues is important to

understand how organizations use these new tools

together with those already established. The search

aims to show if there are publications using the topics

together, as it is known that each subject researched in

isolation brings many publications.

After data collection, all data were transferred to

Microsoft Excel

in different spreadsheets organized

by journals, and formulas and functions were inserted

to filter the data. To demonstrate better visualization,

many graphics were created with the intention of

illustrating the data to facilitate analysis. In addition,

the analysis will also show the most cited journals

using the Google Scholar H5 Index, according to the

four topics mentioned above. It is important to

emphasize that all searches were filtered and the non-

relevant articles, with missing information and other

languages besides English, were not considered; thus,

they were rejected.

In summary, a deep analysis was conducted in the

most relevant database, considering meaningful

aspects such as the number and percentage of

publications by year, number of authors and who are

more productive and influential countries.

Additionally, a research network between significant

authors was created using the VOSviewer software.

In conclusion, we tried to use criteria in a

standardized way, because with that, it is understood

that there is a greater contribution of these issues that

still have much to develop.

4 RESULTS

Main results of the study are introduced in this

section.

4.1 Publications Databases

Figure 3 shows how many citations were found in

Web of Science according to the keywords These

searches yielded only 18 results in total. From the

analysis, it can be observed that the number of

publications on “grid computing” + “data mining”

https://www.microsoft.com/

has 17 registers. It is important to note that the

keywords “utility computing” + “data mining”, and

“utility computing” + “business intelligence” did

not have any results.

More analysis was performed at the Library of

Congress, as shown in figure 4. 7 results were found.

It is important to highlight that "utility computing" +

"data mining" and "utility computing" + "business

intelligence" searches found no results. These results

corroborate those obtained from the Web of Science.

In addition, there are no publications published in

2019 and 2020.

Figure 3: Number of publications per year and by search in

Web of Science.

Figure 4: Number of publications per year and by search in

Library of Congress.

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Searches were also performed on Scopus using

the same keywords. One hundred results were found

(Figure 5), but only “grid computing” + “data

mining” keywords have a significant number of

publications. As shown in Figure 5, the number of

publications over the years has remained balanced,

showing some interest of researchers in these areas.

Figure 5: Number of publications by search and per year in

Scopus.

Finally, searches were made with the four

keywords in PubMed and SciELO, but the databases

retrieved zero results.

The Scopus database is more important in GC and

UC; thus a deeper analysis of Scopus is presented

below.

4.2 Deeper Analysis in Scopus

This search shows a deeper analysis of the Scopus

database, showing the distribution of publications per

year, main authors, institutions, and countries from

2015 to 2020. In addition, research networks will be

presented because this analysis applied to the study of

Figure 6: Number of publications and percentages per year.

the social agents responsible for scientific

publications allows us to identify the number of

members in the network, the intensity of the

relationship between them and the most relevant

members of the network (Andrés, 2009).

Figure 6 shows the distribution of publications

from 2015 to 2020. Some of the associations had no

results and the average number of publications in

general was regular, proving that more research is

necessary in this area.

Figure 7 presents the distribution of the

institutions in the search. There can be observed that

there are 5 institutions with 4 or 3 publications, and

82 institutions with 2 or 1 publications. This reveals

that there are no institutions that clearly stand out

from the others.

Figure 7: Top five institutions and percentages.

Figure 8 presents the countries that contribute the

most to the progress of research in this area. India,

China and the United States were the most relevant

countries.

Figure 8: Total number of publications and percentage per

country.

Subsequently, the authors’ research networks

were analyzed. The dimensions on which the research

networks were assessed were the authors. The

criterion for belonging to a given network was to have

published a significant number of papers in co-

authorship (Andrés, 2009). To create the authors'

Data Mining, Business Intelligence, Grid and Utility Computing: A Bibliometric Review of the Literature from 2015 to 2020

371

research networks, VOSviewer software was used

through the data downloaded from the Scopus

platform. Figures 9 and 10 show the research

networks for the four topics. The thickness of the

lines shows the strength of the connections between

the authors, and the size of the edges shows the

authors with more publications.

In Figure 9, although the number of publications is

insufficient to generate a graph with significant

connections, we can verify that Ortega, A is the

author that connects all the other authors of the

research network for GC + BI.

Figure 9: Research network of Grid Computing and

Business Intelligence authors.

Figure 10: Research network of Grid Computing and Data

Mining authors.

In figure 10, we can verify that Leugh, C. K.;

Braun, P.; and Pazdor, A.G.M. and Cuzzocrea, A., are

the most representative authors of the research

network for GC + DM. Two research networks can be

identified (one represented in blue and the other

represented in red) connected by the author

Cuzzocrea, A.

5 CONCLUSION AND

DISCUSSION

This study makes a pertinent contribution to the

bibliometric analysis. This systematic review

assumes that the researcher follows strict criteria and

a specific methodology to collect and analyze data

from these sources. This study was conducted based

on the results obtained after a bibliometric review,

which helped identify the scope that has given greater

relevance to issues such as DM, BI, GC and UC.

Describing, the relevance of these correlated terms is

due to the fact that there are not enough studies about

this aspect as a way of improving organizations. As a

result, the low number of papers presupposes that few

companies have been using these tools together,

which emphasizes the importance of addressing these

issues in research agendas.

This article analyzes publications between 2015

and 2020, conducted in thousands of publications,

and it was possible to use software such as EndNote,

VOSviewer and Microsoft Excel to obtain and

analyze all data using formulas and functions. It is

important to highlight that bibliometric reviews

cannot be random searches. In other words, different

data sources display different results. Therefore, it is

necessary for the researcher to establish where the

information was extracted from, creating tables and

figures for better visualization. It was observed that

there are not many publications in the area, mainly

concerning utility computing, with only two

publications. Grid computing is more relevant, so we

can infer that the use of grid computing is preferred

to the use of utility computing. Overall, the authors

consider that despite and because of the low number

of publications in the area, this is an area of research

that deserves more attention, because grid and utility

computing is gaining momentum, and it is of

paramount importance to produce research that study

the implementation of data mining and business

intelligence using these types of tools.

There were some limitations to this study. One

main limitation is the low number of publications

available. In addition, when analyzing the Google

Scholar h5-index, the year 2020 was not available at

the time of the search.

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For future research directions, there is a need to

clarify the differences and similarities of grid

computing and utility computing to determine

whether there is really a need to distinguish between

the two. Another direction for the research is two

consider other related keywords in the search such as

“Cloud Computing”, “SaaS”, and IoT, in order to

obtain more relevant studies.

ACKNOWLEDGEMENTS

This work is financed by portuguese national funds

through FCT - Fundação para a Ciência e Tecnologia,

under the project UIDB/05422/2020.

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