Pentaho and Jaspersoft: A Comparative Study of Business Intelligence Open Source Tools Processing Big Data to Evaluate Performances

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

Pentaho and Jaspersoft: A Comparative Study of

Business Intelligence Open Source Tools Processing

Big Data to Evaluate Performances

Victor M. Parra

School of Computing and Mathematics

Charles Sturt University

Melbourne, Australia

Ali Syed

School of Computing and Mathematics

Charles Sturt University

Melbourne, Australia

Azeem Mohammad

School of Computing and Mathematics

Charles Sturt University

Melbourne, Australia

Malka N. Halgamuge

School of Computing and Mathematics

Charles Sturt University

Melbourne, Australia

Abstract—Regardless of the recent growth in the use of “Big

Data” and “Business Intelligence” (BI) tools, little research has

been undertaken about the implications involved. Analytical tools

affect the development and sustainability of a company, as

evaluating clientele needs to advance in the competitive market is

critical. With the advancement of the population, processing

large amounts of data has become too cumbersome for

companies. At some stage in a company’s lifecycle, all companies

need to create new and better data processing systems that

improve their decision-making processes. Companies use BI

Results to collect data that is drawn from interpretations

grouped from cues in the data set BI information system that

helps organisations with activities that give them the advantage

in a competitive market. However, many organizations establish

such systems, without conducting a preliminary analysis of the

needs and wants of a company, or without determining the

benefits and targets that they aim to achieve with the

implementation. They rarely measure the large costs associated

with the implementation blowout of such applications, which

results in these impulsive solutions that are unfinished or too

complex and unfeasible, in other words unsustainable even if

implemented. BI open source tools are specific tools that solve

this issue for organizations in need, with data storage and

management. This paper compares two of the best positioned BI

open source tools in the market: Pentaho and Jaspersoft,

processing big data through six different sized databases,

especially focussing on their Extract Transform and Load (ETL)

and Reporting processes by measuring their performances using

Computer Algebra Systems (CAS). The ETL experimental

analysis results clearly show that Jaspersoft BI has an increment

of CPU time in the process of data over Pentaho BI, which is

represented by an average of 42.28% in performance metrics

over the six databases. Meanwhile, Pentaho BI had a marked

increment of the CPU time in the process of data over Jaspersoft

evidenced by the reporting analysis outcomes with an average of

43.12% over six databases that prove the point of this study. This

study is a guiding reference for many researchers and those IT

professionals who support the conveniences of Big Data

processing, and the implementation of BI open source tool based

on their needs.

Keywords—Big Data; BI; Business Intelligence; CAS;

Computer Algebra System; ETL; Data Mining; OLAP

I. INTRODUCTION

Business Intelligence software converts stored data of a

company’s clientele profile and turns it into information that

forms the pool of knowledge to create a competitive value and

advantage in the market it is in [1]. Additionally, Business

Intelligence is used to back up and improve the business with

reasonable data and use the analysis of this data, to

continuously improve an organisation’s competitiveness. Part

of this analysis is to provide timely reports, for management’s

to make the decision based on factual information, so their

decision-making is based on concrete evidence. Howard

Dresner, from Gartner Group [2], was the first to coin the term

Business Intelligence (BI), as a term to define a collection of

notions and procedures to support the decision-making, by

using information found upon facts.

BI system gives enough data to use and evaluate the needs

and desires of customers and in addition it allows to: [3]: i)

Design reports for departments or global areas in a company,

ii) Build a database for customers, iii) Create scenarios for

decision-making, iv) Share information between areas or

departments of a company, v) Sandbox studies of

multidimensional designs, vi) Extract, transform and process

data, vii) Give a new approach to decision-making and viii)

Improve the quality of customer service.

The benefits of systemizing BI include the amalgamation

of information from several sources. [4], creating user profiles

for information management, reducing the dependence on the

systems department, the reduction in the time of obtaining

information, improves the analysis, and also improves the

availability to access real-time information according to

specific current business criteria.

The recent publication of Gartner Magic Quadrant for

Business Intelligence Platforms 2015 [5] has highlighted the

changes being taken by the BI sector to rapidly deploy

20 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

platforms that can be used by both business users and analysts

to extract information from collected data. Traditionally,

business intelligence has been understood as a set of

methodologies, applications and technologies used to

transform data into information and then information into a

personal profile of clients that is generated into structured data

to serve different areas of business enterprise [6].

Therefore, Big Data will aid to develop better procedures

that allow (BI) tools to be used to gather information, such as

[7]: i) Process and analyse volumes of information; ii)

Increase the universe of data to consider when decision-

making: and inherent historical data of the company, to

incorporate data from external sources ; iii) Provide an

immediate response to the continued provision of real-time

data of the devices and the possibilities of interconnections

between devices; iv) Working with structures of complex and

heterogeneous data: logs, emails, conversations, locations,

voice, etc.; v) and lastly, to Isolate from the physical

constraints of storage and process by making use of scalable

solutions and high availability at a competitive prices.

This paper presents an experimental analysis of the

comparison of two of the best positioned open source BI

systems in the market: Pentaho and Jaspersoft, processing Big

data and focussing on their Extract Transform and Load (ETL)

and reporting processes by measuring their performance using

Computer Algebra Systems. The aim of this paper is to

analyse and evaluate these tools and outline how they improve

the quality of data, and inadvertently helps us understand the

market conditions to make future predictions base on trends.

Section II describes the capabilities and components of

both Pentaho and Jaspersoft BI Open Sources. Section III

introduces the computer algebra systems SageMath and

Matlab. This is followed by the materials and methods

(Section IV) used in the analysis and experimentation,

especially the ETL and Reporting measurements and how they

were implemented. In Section V, the results of the study for

CPUtime as a function of the "size" from the input data for the

ETL and Reporting processes from both Pentaho and

Jaspersoft Business Intelligence Open Sources, applying two

different Computer Algebra Systems. Section VI contains the

discussion of the experimentation. Section VII, the conclusion

of the study.

II. P

ENTAHO AND JASPERSOFT BUSINESS INTELLIGENCE

OPEN SOURCES

A. Pentaho

Pentaho, created in 2004 is the current leader of Business

Solutions Intelligence Open Source. It offers its own solutions

across the spectrum of resources to develop and maintain the

operations of BI projects from the ETL with data integration

to the dashboards with Dashboard Designer [8]. Pentaho has

built its solution Business Intelligence integrating different

existing and recognized solvency projects. Data Integration

was previously known as Kettle; indeed, it retains its old name

as a colloquial name. Mondrian is another component of

Pentaho that retaining its own entity.

Pentaho has the following components:

a) ETL: Pentaho Data Integration (previously Kettle) is

one of the most widely used ETL solutions and better valued

in the market [9]. It has a long history, solidity, and robustness

that make it a highly recommended tool. It allows

transformations and works in a very simple and intuitive way,

as it is shown in Fig. 1. Likewise, the Data Integration projects

are very easy to maintain.

Fig. 1. Pentaho Data Integration Interface, the ETL solution allows

transformations and works in a very simple and intuitive way

b) Web Application-BI Server: The BI Pentaho Server is

a 100% Java2EE allows us to manage all BI resources [10]. It

has a BI user interface available where reports are stored,

OLAP views and dashboards as it is illustrated in Fig. 2. In

addition, it offers access to a management support that allows

managing and monitoring both application and usage.

Fig. 2. Pentaho Server User Interface to manage BI resources where all the

reports are founded, OLAP views, and dashboards. Also the access to a

management supports that allows managing and monitoring both application

and usage

c) Pentaho Reporting: Pentaho provides a

comprehensive reporting solution. Covering all aspects needed

in any reporting environment, as shown in Fig. 3. The Pentaho

reporting tool is the old form of JFreeReport [11]: i) It

provides a tool for reporting (Pentaho Reporting), ii) Provides

an execution engine, iii) Provides Metadata tool for

conducting reports Ad-hoc, and iv) Provides a user interface

that allows ad-hoc reports (WAQR).

21 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

Fig. 3. Pentaho Reporting Interface with a comprehensive reporting

solution, covering all aspects needed in any reporting environment

d) OLAP Mondrian: Online Analytical Processing is the

technology that allows us to organize information in a

dimensional structure that will allow us to move information

by scrolling through its dimensions [12]. Mondrian is the

Pentaho OLAP engine. Although it can be integrated

independently on any other platform, and indeed it is the

component. Data Integration that is used independently.

Mondrian is a Hybrid OLAP engine that combines the

flexibility of ROLAP engines with a cache that provides

speed.

• Viewer OLAP: Pentaho Analyser: OLAP Viewer that

comes with the Enterprise version [13]. Modern and

easier to use than JPivot as it is illustrated in Fig. 4.

AJAX provides an interface that allows great flexibility

when creating the OLAP views.

Fig. 4. Pentaho Analyser Interface to create OLAP views that provide a

comprehensive reporting solution. Covering all aspects needed in any

reporting environment

e) Dashboards: Pentaho provides the possibility of

making dashboards [13] through the web interface using the

dashboard designer as it is shown in Fig. 5.

Fig. 5. Pentaho Dashboards Interface that provides the possibility of making

dashboards through the web interface by using the dashboard designer

B. Jaspersoft

Jaspersoft is the company behind the famous and extended

Jaster Reports. Open Source reporting solution preferred by

most developers to embed in any Java application that requires

a reporting system. Jaspersoft has built its solution B.I. around

its reporting engine [14]. This has been done differently from

Pentaho. Jasper has integrated its projects that also solves

existing and consolidates projects nonetheless, has not

absorbed it. This strategy makes it "depend" on Talend

solution regarding ETL and Mondrian - Pentaho for the OLAP

engine. Jasper has access to the code Mondrian that can adapt

and continue its developments with Mondrian.

Jaspersoft has the following components:

a) ETL - JasperETL is actually Talend Studio. Talend,

unlike Kettle, it has not been absorbed by Jasper and remains

an independent company that offers its products independently

[15]. Working with Talend is also quite user-interface intuitive

and proprietary although the approach is completely different.

Talend is a code generator that is the result of an ETL exercise

and it is native Java or Perl code. It can also compile and

generate Java procedures or instructions. Talend is more

oriented to a type of programmer used with a higher level of

technical expertise than it requires by Kettle as it is illustrated

in Fig. 6. To sum up, the flexibility is much better with this

approach.

Fig. 6. Jaspersoft ETL Interface is actually Talend Studio, it is also quite

user-interface intuitive and a code generator

22 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

b) Web Application–JasperServer: JasperServer is a

100% Java2EE that allows us to manage all BI resources [16].

The overall look of the web application is a bit minimalist

without sacrificing the power as shown in Fig. 7. However,

having all resources available on the top button bar makes it a

100% functional application and has all the necessary

resources for BI.

Fig. 7. Jaspersoft Server Interface is a 100% Java2EE to manage business

intelligence resources

c) Reports: As described, the report engine is the

solution of Jaspersoft as it is illustrated in Fig. 8. The

component provides features such as i) Report development

environment: Ireport as a system based on environment

NetBeans. What makes it challenging to machine resources?

In return it offers great flexibility, ii) System of metadata

(Domains) the web. These, along with ad-hoc reports, are the

strengths of this solution, iii) Web Interface for ad-hoc reports

really well resolved, iv) The runtime JasperReports widely

was known and used in many projects where a solvent

reporting engine is needed, and v) The reports can be exported

into PDF, HTML, XML, CSV, RTF, XLS and TXT.

• Predefined – Ireport: IReport is a working

environment that allows a large number of features

[17]. Here something like that Talend is a working

environment with larger demands as a result of offering

a number of possibilities occurs.

• Ad hoc: This is the real strength of Jasper solutions.

The editor of ad-hoc reports is the best structured and

best featured tool for analysing [17]. It offers: i)

Selection of different types of templates and formats,

ii) Selection of different data sources, iii) Validation

consultation on the fly, iv) Creation of reports by

dragging fields to the desired location: i) Tables, ii)

Graphics, iii) Crosstable (Pivot), and iv) Edition of all

aspects of the reports.

Fig. 8. Jaspersoft Reporting Interface that includes report development

environment, a system of metadata (Domains) web, web interface for ad-hoc

reports and runtime JasperReports

d) OLAP: The OLAP engine that uses JasperServer is

Mondrian and uses a Viewfinder-JasperAnalysis [18], which

is no longer JPivot but with a layer of makeup as shown in

Fig. 9. Already mentioned in Pentaho paragraph.

Fig. 9. The OLAP engine that uses JasperServer is Mondrian and uses a

Viewfinder-JasperAnalysis which is no longer JPivot but with a layer of

makeup

e) Dashboards: Dashboard Designer. Illustrated in

Fig.10.

• Predefined: They do not make much sense, given the

designer panels [19]. In any case, to be a Java platform

it can always include proper developments.

• Ad-hoc: Dashboard Designer: It is back to a really easy

and simple use of the web editor.

23 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

Fig. 10. Jaspersoft Dashboards Designers Interface able to select predefined

or ad-hoc

III. COMPUTER ALGEBRA SYSTEMS

A. Sagemath

SageMath is a computer algebra system (CAS) that is built

on mathematical packages and contrasted as NumPy, Sympy,

PARI / GP or Maxima. It accesses the combined power of the

same through a common language based on Python. The

interaction code combines cells with graphics, texts or

formulas enriched with LaTeX rendered. Additionally,

SageMath is divided into a core that performs calculations and

an interface that displays and interacts with the user. Even, a

command line-based text is also available using Python that

allows interactive control calculations [20]. The Python

programming language supports object-oriented expressions

and functional programming. Internally, SageMath is written

in Python and a modified version of Pyrex called Cython. It

allows parallel processing [21] using both multi-core

processors and symmetric multiprocessors. It also provides

interfaces to other non-free software as Mathematica, Magma,

and Maple (undistributed with SageMath) that allows users to

combine software and compare results and performances.

All the packages cover most features such as i) Libraries of

elementary and special functions, ii) 2D and 3D graphs of

both functions and data, iii) Data manipulation tools and

duties, iv) A toolkit for adding user interfaces to calculations

and apply, v) Tools for image processing using Python and

Pylab, vi) Tools to visualize and analyze graphs, vii) Filters

for importing and exporting data, images, video, sound, CAD,

and GIS, viii) Sage embedded in documents LaTeX6 [22].

B. Matlab

Matlab is a computer algebra system (CAS) that provides

an integrated environment that develops and offers

representative characteristics such as the implementation of

algorithms, data representation and functions. Also,

communication with programs in other languages and other

hardware devices [23], among others are advanced. The

Matlab package has two extra tools that extend those

functionalities: Simulink is a platform for multi-domain

simulation and GUIDE that is a graphic user interface - GUI.

Additionally, its potential could be expanded using Matlab

toolboxes; and Simulink blocks with block sets.

The language of Matlab is interpreted, and can run in both

interactive environments through a script file (*.m files). This

language allows vector and matrix operations to function,

lambda calculus, and object-oriented programming. An

additional tool called Matlab Builder has been launched that

contains an "Application Deployment" which allows using

Matlab functions, as library files, that provides the ability to

be used with environments such as .NET or Java. Matlab

Component Runtime (MCR) should be used on the same

machine where the main application is set for the Matlab

function properly [24]. One of the versatilities of this CAS is

that it is quite useful to carry out measurements and it

provides an interface to interact with other programming

languages. Thus, Matlab can call functions or subroutines

written in C or FORTRAN [25]. As the process is

accomplished by, creating a wrapper function that allows them

to be passed and returned by their data types Matlab.

IV. M

ATERIALS AND METHODS

Accurately measuring the processing times is not a trivial

task, and the results may vary significantly from one computer

to another. The number of factors that influence the execution

times has used an algorithm, operating system, processor

speed, the number of processors and instruction sets that

understands the amount of RAM, and cache, and speed of

each, math coprocessor, GPU Among each other. Even on the

same machine, the same algorithm sometimes takes much

longer to give results, due to factors such as using more time

than other applications, or if there is enough RAM when

running the program.

The objective is to compare only the ETL and Reporting

processes, trying to draw independent conclusions from one

machine to another. The same algorithm can be called with

different input data.

The goal of this study is to measure the run-time as a

function of the "size" of the input data. For this, two

techniques are used: - Measure run time of programs with

different input data sizes and - Count the number of operations

performed by the program.

A. ELT Measurement

With Sage was measured the run time and efficiency of

ETL processes in both BI tools mentioned previously as it is

illustrated in Fig. 11. For the CPU time, Sage uses the

concepts of CPU time and Wall time [12], which are the times

that the computer is dedicated solely to the program.

The following flow chart shows the ETL process

measurement.

24 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

Fig. 11. The ETL process measurement using the Sage computer algebra

system for all databases

The CPU time is dedicated to our calculations, and Wall

time clock time between the beginning and the end of the

calculations. Both measurements are susceptible to

unpredictable variations. The easiest way to get the run time

of a command is to put the word time to the command as it is

shown in Fig. 12.

Fig. 12. Sage code to measure CPU time of ETL processes in both BI tools

for small and higher data sizes

The time command is not flexible enough and needs the

CPUtime functions and Walltime. CPUtime is a kind of meter:

a meter progresses as the calculations are done, and moves

many seconds as the CPU dedicated to Sage. The Walltime is

a conventional clock (the clock UNIX). For the time spent on

the program, also the before and after times of the execution

were recorded and calculated and the differences are

illustrated in Fig. 13.

Fig. 13. Sage code using CPUtime functions and Walltime to measure the

ETL process in both tools

The following code saves the list of the CPU times used to

run the factorial function with data of different sizes as shown

in Fig. 14.

Fig. 14. Sage code to save lists of the CPU times used to run the factorial

function with data of different sizes

B. Reporting Measurements

With Matlab the measured run time and efficiency of

Reporting processes in both BI tools mentioned previously is

shown in the flow chart in Fig. 15.

25 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

Fig. 15. The Reporting measurement process using the Matlab computer

algebra system for all databases

For doing this activity, a C function was used that

implemented a High-Resolution Performance Counter for

measurement the Reporting processes on both BI tools as it is

illustrated in Fig. 16.

Fig. 16. Code to measure the reporting process

In this case, Query Performance Counter acts as a clock ()

and Query Performance Frequency as CLOCKS_PER_SEC.

That is the first function that gives the counter value and the

second frequency (in cycles per second, hertz). It is clear that

an LARGE_INTEGER is a way to represent a 64-bit integer

by a union.

C. Databases Analysis

Six different Excel databases with different sizes have

been used to perform the analysis. Those databases were

acquired from UCI Machine Learning Repository [26] and

their main features are described in Table 1:

TABLE I. DESCRIPTION OF THE SIX EXCEL DATABASES INCLUDING

THEIR NUMBER OF ATTRIBUTES, INSTANCES AND SIZES

Database

Number of

Attributes

Number of

Instances

Size

DB 1

65.055

0.009 Mb

DB 2

118.439

0.017 Mb

DB 3

609.287

0.134 Mb

DB 4

999.231

1.321 Mb

DB 5

1.458.723

35.278 Mb

DB 6

2.686.655

144.195

D. Computer system

In order to perform the experiment and examination, the

Business Intelligence Tools, Computer Algebra Systems and

Databases are set on and customised in a PC with the

following features: i) Operating system: x64-based PC, ii)

Operating system version: 10.0.10240 N/D iii) Compilation

10240, iv) Number of processors: 1, v) Processor: Intel (R)

Core (TM) i5-3317U vi) Processor speed: 1.7 GHz, vii)

Instructions: CISC, viii) RAM: 12 Gb, ix) RAM speed: 1600

MHz, x) Cache: SSD express 24 Gb, xi) Math coprocessor:

80387, xii) GPU: HD 400 on board.

V. R

ESULTS

In this study, results for CPUtime as a function of the

"size" was obtained from the data input for the ETL and

Reporting processes from both Pentaho and Jaspersoft

Business Intelligence Open Sources, applying two different

Computer Algebra Systems.

The measurements of the computational times might

fluctuate considerably based on many factors such as the used

algorithm, operating system, processor speed, number of

processors and instruction set that understand the amount of

RAM, and cache, of each speed, along with math coprocessor,

GPU among others. Even on the same machine, the same

algorithm sometimes takes much longer to give the result of

others, due to factors that it is more time-consuming than

other applications that are running or if it has enough RAM

when running the program.

Tables 2 and 3 shows the results of the CPU time (in

minutes) of the ETL and Reporting processes and they present

the times it took per tool in processing the different sized

databases. Additionally, the increment of processing data can

be considered as a difference between those BI tools in

process. As a result of the first examination (Table 2), it is

clear that the computational times for Pentaho ETL process

measured by Sage were: 8 min; 12.01 min; 21 min; 32.01 min;

39.06 min and 48.01 min. Conversely, the computational

times for Jaspersoft, were 9.54 min; 19.32 min; 31.88 min;

44.73 min; 55 min and 67.69 min, processing 0.009 Mb from

DB1; 0.017 Mb from DB2; 0.134 Mb from DB3; 1.321 Mb

from DB4; 35.278 Mb from DB5 and 144.195 Mb from DB6,

respectively for both tools.

26 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

The results of the CPU time of the ETL process is shown

in Table 2 and it presents the times that it took per tool in

different databases.

TABLE II. RESULTS OF THE CPU TIME OF THE ETL PROCESS WITH THE

TIMES TOOK PER TOOL AND THE INCREMENT IN THE PROCESS DATA IN THE

DIFFERENT DATABASES

Tool Process

Time (Minutes)

DB1

DB2

DB3

DB4

DB5

DB6

Pentaho

ETL

12.01

32.01

39.06

48.01

Jaspersoft

ETL

9.54

19.32

31.88

44.73

67.69

Increment in the Process of Data

Jaspersoft

ETL

DB1

DB2

DB3

DB4

DB5

DB6

19.2

60.85

51.79

39.75

40.77

40.99

On the other hand, as a result of the second examination

(Table 3), we can detect and see that the result of the Pentaho

Reporting process measured by Matlab was: 3.75 min; 5.35

min; 8.47 min; 12.03 min; 17.07 min and 22.60 min.

Conversely, the reporting process for Jaspersoft were 3 min;

4.02 min; 6.05 min; 8.13 min; 11.16 min and 14.15 min,

processing 0.009 Mb from DB1; 0.017 Mb from DB2; 0.134

Mb from DB3; 1.321 Mb from DB4; 35.278 Mb from DB5

and 144.195 Mb from DB6, respectively for both tools. The

results of the CPU time of the Reporting process are shown in

Table 3 and it presents the time it took per tool in different

databases.

TABLE III. RESULTS OF THE CPU TIME OF THE REPORTING PROCESS WITH

THE

TIMES TOOK PER TOOL AND THE INCREMENT IN THE PROCESS DATA IN

THE

DIFFERENT DATABASES

Tool Process

Time (Minutes)

DB1

DB2

DB3

DB4

DB5

DB6

Pentaho

Reporti

3.75 5.35 8.47 12.03 17.07 22.60

Jaspersoft

Reporti

3 4.02 6.05 8.13 11.16 14.15

Increment in the Process of Data

Pentaho

Reporti

DB1

DB2

DB3

DB4

DB5

DB6

25%

32.99

40% 48% 53%

59.75

The Graphical comparison results of the CPU times for the

ETL and Reporting processes performed by the BI tools,

accessing six different sized databases which is illustrated

below. In Fig. 17, we observe that Jaspersoft has significantly

increased the results of the CPU time of the ETL process

represented by 19.22%, 60.85% 51.79%, 39.75%, 40.77 and

40.99% processing DB1, DB2, DB3, DB4, DB5 and DB6,

respectively. This means that in the ETL process, Pentaho had

a better performance than Jaspersoft.

Fig. 17. CPU Time of the ETL process with the times it took per tool in

processing data in the different databases

In Fig. 18, it is evident that Pentaho had a considerable rise

in the outcomes of the Reporting process denoted by 25%,

32.99%, 40%, 48%, 53% and 59.75% processing DB1, DB2,

DB3, DB4, DB5 and DB6, correspondingly. In this case,

Jaspersoft had a better performance than Pentaho in the

Reporting process.

Fig. 18. CPU Time of the Reporting process with the times took per tool in

the process data in different databases

The outcomes also showed that both results of CPU time

of the ETL and Reporting process are directly related to the

sizes of the databases. What is more, is that the study could

identify that Pentaho had a superior performance for ETL

process and Jaspersoft an improved performance for

Reporting process.

VI. D

ISCUSSION

The ETL experimental analysis results clearly shows that

Jaspersoft BI had an increment of CPU time in the process of

data over Pentaho BI, represented in an average of 42.28% of

performance metrics over six databases. Pentaho provided its

data integration and ETL capabilities as shown in [9], having a

27 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

better performance. Evidently, for this part of the

experimentation, our study has demonstrated that Pentaho had

higher performance ETL capabilities with the aim of covering

the whole data integration requirements, simultaneously by

big data as well. That high performance is provided by its

parallel processing engine and these features are shown in

[11].

Pentaho BI had a marked increment of the CPU time in the

process of data over Jaspersoft evidenced by the Reporting

analysis outcomes with an average 43.12% over six databases.

Clearly, in this part of the examination, the analysis has

confirmed that Jaspersoft has had a higher performance

Reporting capability with the objective of generating reports.

This particular feature is aligned with other studies, which

argue that Jaspersoft extends the range of its BI requirements

including reporting based on its operational production,

interactive end-user query, data integration and analysis as

shown in [11]. On top of this, investigating various security

features [27-29] could be an interesting avenue to explore in

the future to protect BigData.

VII. C

ONCLUSION

This study has tested two of the best positioned open

source Business Intelligence (BI) systems in the market:

Pentaho and Jaspersoft. Both BI systems present notable

features on their components. Pentaho on one side along with

ETL component with great usability, maintainability and

flexibility in making the transformations: Web Application

with Java j2EE application 100% extensible, adaptable and

configurable; the configuration management is integrated in

most environments, that communicate with other applications

via web services; it integrates all the information resources

into a single operating platform; Reports with an intuitive tool

that allows clients to create reports easily; OLAP Mondrian

with a consolidated engine widely used in environments of

JAVA; Dashboard Designer makes dashboards Ad-hoc,

dashboards based on SQL queries or Metadata and a great

freedom by offering a wide range of components and options.

Jaspersoft on the other side has JasperETL (Talend) with

Java / Perl native, Web Application with a Java j2EE

application 100% extensible, adaptable and customizable; the

management settings are very well resolved, it allows almost

all through the same Web application; It integrates all

information resources into a single operating platform; the

editor Ad-hoc reports and Box Editor Ad-hoc command are

best resolved; Reports are fast; Ad hoc and have a nice

interface, with good flexibility and power, simple, intuitive

and easy to use.

The experimental analysis has focussed on their ETL and

Reporting processes by measuring their performance s using

the two Computer Algebra Systems, Sage and Matlab. During

the ETL analysis results, clearly showed that it could observe

Jaspersoft BI and has an increment of CPU time in the process

of data over Pentaho BI, represented in an average of 42.28%

of performance metrics over six databases. Meanwhile,

Pentaho BI had a marked increment CPU time in the process

of data over Jaspersoft evidenced by the Reporting analysis

outcomes with an average 43.12% over the databases. This

study is a useful reference for many researchers and those who

are supporting decisions of Big Data processing and the

implementation of BI open source tool based on their process

expectations. The future work of the author would involve

new studies and implementations of BI with Data warehousing

to create a technological tool to support the decision-making

at the enterprise level by taking this paper as a base.

REFERENCES

[1] B. List, R. M. Bruckner, K. Machaczek, J.Schiefer, “A Comparison of

Data Warehouse Development Methodologies Case Study of the Process

Warehouse,” in Database and Expert Systems Applications - DEXA

2002, France, 2002.

[2] H. Dresner, “Business intelligence: competing Against Time.,” in

Twelfth Annual Office Information System Conference, London, 1993.

[3] S. Atre, L. T. Moss, “Business Intelligence Roadmap: The Complete

Project Lifecycle for Decision-Support Applications, Addison Wesley

Professional”, 2003.

[4] J. F. Gonzalez, “Critical Success Factors of a Business Intelligence

Project,” Novática, no. 211, pp. 20-25, 2011.

[5] R. L. Sallam, B. Hostmann, K. Schegel, J. Tapadinhas, J. Parenteau, T.

W. Oestreich, “Magic Quadrant for Business Intelligence and Analytics

Platforms”, 23 February 2015. [Online]. Available:

www.gartner.com/doc/2989518/magic-quadrant-business-intelligence-

analytics. [Accessed 9 Aug 2016].

[6] Gartner, Inc., “IT Glossary”, 2016. [Online]. Available:

http://www.gartner.com/it-glossary/business-intelligence-bi/. [Accessed

9 Aug 2016].

[7] R. Kune, P. K. Konugurthi, A. Agarwal, R. R. Chillarige, R. Buyya,

“The Anatomy of Big Data Computing”, Software: Practice and

Experience, pp. 79-105, 2016.

[8] Pentaho A Hitachi Group Company, “Pentaho | Data Integration,

Business Analytics and Bid Data Leaders”, Pentaho Corporation, 2005-

2016. [Online]. Available: www.pentaho.com. [Accessed 10 Aug 2016].

[9] D. Tarnaveanu, “Pentaho Business Analytics: a Business Intelligence

Open Source Alternative”, Database System Journal, vol. III, nº 3/2012,

p. 13, 2012.

[10] T. Kapila, “ Pentaho BI & Integration with a Custom Java Web

Application”, 2014. [Online]. Available:

www.neevtech.com/blog/2014/08/13/pentaho-bi-integration-with-a-

custom-java-web-application-2/. [Accessed 11 Aug 2016]

[11] Innovent Solutions, “Pentaho Reports Review ”, 2016. [Online].

Available: www.innoventsolutions.com/pentaho-review.html.

[Accessed: 12 Aug 2016].

[12] G. Pozzani, “OLAP Solutions using Pentaho Analysis Services”, 2014.

[Online]. Available:

www.profs.sci.univr.it/~pozzani/attachments/pentaho_lect4.pdf.

[Accessed: 12 Aug 2016].

[13] Sanket, “Fusion Charts Integration in Pentaho BI Dashboards”, 2015.

[Online]. Available: www.fusioncharts.com/blog/2011/05/free-plugin-

integrate-fusioncharts-in-pentaho-bi-dashboards/. [Accessed: 13 Aug

2016].

[14] TIBCO Jaspersoft, “Jaspersoft Business Intelligence Software”, TIBCO

Software, 2016. [Online]. Available: www.jaspersoft.com. [Accessed 15

Aug 2016].

[15] S. Vidhya, S. Sarumathi, N. Shanthi, “Comparative Analysis of Diverse

Collection of Big data Analytics Tools”, International journal of

Computer, Electrical, Automation, Control and Information

Engineering, vol. 8, nº 9, p. 7, 2014.

[16] T. olavsrud, “Jaspersoft Aims to Simplify Embedding Analytics and

Visualizations”, 2014. [Online]. Available:

www.cio.com/article/2375611/business-intelligence/jaspersoft-aims-to-

simplify-embedding-analytics-and-visualizations.html. [Accessed: 16

Aug 2016]

[17] S. Pochampalli, “Jaspersoft BI Suite Tutorials”, 2014. [Online].

Available:www.jasper-bi-suite.blogspot.com.au/. [Accessed: 17 Aug

2016].

28 | Page

www.ijacsa.thesai.org

(IJACSA) International Journal of Advanced Computer Science and Applications,

Vol. 7, No. 10, 2016

[18] J. Vinay, “OLAP Cubes in Jasper Server”, 2013. [Online]. Available:

www.hadoopheadon.blogspot.com.au/2013/07/setting-up-olap-cubes-in-

jasper.html. [Accessed: 19 Aug 2016.]

[19] Informatica Data Quality Unit, “Data Quality: Dashboards and

Reporting”, 2013. [Online]. Available:www.

Markerplace.informatica.com/solution/data_quality_dashBoards_andrep

orting-961. [Accessed: 21 Aug 2016]

[20] Sagemath, “Sagemath | Open-Source Mathematical Software System”,

Sage, 20 March 2016. [Online]. Available: www.sagemath.org.

[Accessed 21 Aug 2016].

[21] AIMS Team, “Sage”, 2016. [Online]. Available:

www.launchpad.net/~aims/+archive/ubuntu/sagemath. [Accessed: 21

Aug 2016.]

[22] W. Stein, “The Origins of SageMath”, 2016. [Online]. Available:

www.wstein.org/talks/2016-06-sage-bp/bp.pdf. [Accessed: 28 2016.]

[23] MathWorks, “MATLAB - MathWorks - MathWorks Australia”,

MathWorks, 3 March 2016. [Online]. Available:

www.au.mathworks.com. [Accessed 28 Aug 2016].

[24] M. S. Gockenbach, “A Practical Introduction to Matlab”, 1999.

[Online]. Available: www.math.mtu.edu/~msgocken/intro/intro.html.

[Accessed: 28 Aug 2016]

[25] K. Black, “Matlab Tutorials”, 2016. [Online]. Available:

www.cyclismo.org/tutorial/matlab/. [Accessed: 29 Aug 2016].

[26] M. Lichman, “UCI Machine Learning Repository”, 2013. [Online].

Available: www.archive.ics.uci.edu/ml [Accessed 10 Aug 2016].

[27] D. V. Pham, A. Syed, A. Mohammad and M. N. Halgamuge, “Threat

Analysis of Portable Hack Tools from USB Storage Devices and

Protection Solutions", International Conference on Information and

Emerging Technologies, pp 1-5, Karachi, Pakistan, 14-16 June 2010.

[28] D. V. Pham, A. Syed and M. N. Halgamuge, “Universal serial bus based

software attacks and protection solutions, Digital Investigation 7, 3, pp

172-184, 2011.

[29] D. V. Pham, M. N. Halgamuge, A. Syed and P. Mendis. “Optimizing

windows security features to block malware and hack tools on USB

storage devices”, Progress in electromagnetics research symposium,

350-355, 2010.

29 | Page