Description
Progress in Business Intelligence System research
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 96
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
Progress in Business Intelligence System research : A literature Review
Rina Fitriana
1
, Eriyatno
2
, Taufik Djatna
3
1
Department of Industrial Engineering
Trisakti University
Jakarta Indonesia
[email protected]
2,3
Dept of Agro-Industrial Technology
Bogor Agriculture University
Bogor ,West Java, Indonesia
[email protected], [email protected]
Abstract—This paper reviews the literature of Progress in Business Intelligence System research. Relates articles
appearing in the international journal like Proquest, Ebscohost, Emerald, Science Direct and IEEE Conference from 2000 to
2011 are gathered and analyzed so the following three question can be answered
1)Which approached were most
pupular?(ii)Which the most popular BI integrated research. It was found 50% research is in single approach Business
Intelligence System. Integrated research between Business Intelligence and Data Mining is the most popular evaluating
criteria with 6,67 %. The topic that integrated with BI research that is found in this research is Supply Chain Management,
Customer Relationship Management, Data Mining, Data Warehouse, Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management, Artificial Intelligence, Enterprise Resource Planning, Extract
Transformation Loading, OLAP, Quality Management System, Strategic Management.
Keywords: business intelligence, data mining, data warehouse, decision support system, supply chain management, artificial
intelligence, quality management system
I. INTRODUCTION
Business Intelligence is a process for extracting,
transforming, managing and analyzing large data by make a
mathematical model to gain information and knowledge to
help make decisions in the complex. Elements of Business
Intelligence are Data Warehouse, Data Mining and Decision
Support System. There are many integrated topic that is
integrated in Business Intelligence System research.
The general objective of this research is to make
literature review of Progress in Business Intelligence System
research. This paper reviews the 60 journals of business
intelligence. Relates articles appearing in the international
journal like Proquest, Ebscohost, Emerald, Science Direct
and IEEE Conference from 2000 to 2011 are gathered
The paper is organized as follows: Section 2 dan 3
describe the individual approaches and integrated approaches
critically respective. Section 4 analysis the most prevalently
used approaches discuss the most popular integrated research
of Business Intelligence. Section 5 suggested for future
work. Section 6 concludes the paper.
II. INDIVIDUAL APPROACH
A. Business Intelligence System
46,67 % papers discuss about individual approach the
theoretic, method, software of business intelligence system.
The papers writes the definition, methodology, architecture,
case study, software that used in business intelligence
system.
Manufacturing resource management system (MRMS)
analyzes the current situation of business environment and
business intelligence systems framework at first, and studies
the theoretic and methods about the business intelligence
system and analyzes the necessity of an automated
negotiation method in the based on the manufacture
requirement and latency manufacturing resource state, order
enterprise can find forwardly or passively some
manufacturers satisfying it manufacturing tasks requirement
taking into account justice, multi-topics influence genes, and
negotiation both sides preference, etc. [55]
Evolutional objectives of BI system in E-business,
explores the application way of BI and the working
mechanism of BI system in E-business ,and more expounds
the its operational framework of E-business intelligence
system. [17]
CMMI (Capability Maturity Model Integrated) and
makes a contribution on the empirical knowledge on CMMI.
CMMI is developed to define different levels of software
process maturity. The concepts underlying CMMI have been
defined different maturity levels for a business intelligence
process.[33]
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The critical success factor in business intelligence
system success seeks to bridge the gap that exist between
academia and practitioner by investigating.[48]
The current applied status of business intelligence and
multi-agent technology and design of the low-cost business
intelligence system based on multi-agent is put forward,
which is composed of the low-cost business intelligence
system framework, the analysis of the core components'
function and the operation mechanism of the system.[50]
Even-Driven Architecture (EDA) based Right-Time
Business Intelligence System Framework (EDA based RT-
BISF), which combines RT-BI and the business process
based on the EDA and Agent, to resolve the environment
uncertainty, business dynamics and to meet the needs of
dynamic adjustment of business solution for the enterprise in
the fierce competitive environment.[19]
An Enterprise Marketing Campaign Automation
(EMCA) system that can provide data for businesses to
instantly assemble them for determining effective and
accurate marketing campaign strategy. By generating a
mailing list targeted to a specific group of buyers with
reference to their buying habits can reduce marketing cost by
just mailing the promotional items to the specific group of
buyers. [7]
A Business Intelligence (BI) development project applied
to Homogeneous Diagnostic Groups (GDH) which are very
specific and important for health management. The main
goal of this project was make available data in a simple way
for end users to have a support decision tool that increases
the performance of decision making. [3]
How these scenarios impact information quality in
business intelligence applications and lead to nontrivial
research challenges. They describe the idea of uncertain
events and key indicators and present a model to express and
store uncertainty and a tool to compute and visualize
uncertain key indicators.[41]
The current situation of business environment and
business intelligence systems (BIS) framework at first, and
studies the theoretic and methods about the business
intelligence system based on ontology. Based on ontology,
this paper proposes an integration framework for business
intelligence systems. [47]
A first of a kind system, called business intelligence from
voice of customer (BIVoC), that can: 1) combine
unstructured information and structured information in an
information intensive enterprise and 2) derive richer business
insights from the combined data. [46]
The commercialization of a business intelligence
application deploying computational intelligence techniques.
Theoretical foundations are included where appropriate,
along with implementation and comparative benchmark
results. Discussions on technology transfer mechanisms are
included, identifying a generic framework for the
commercialization of technology innovations, with a
particular case study from Jordan .[2]
The application framework of enterprise business
intelligence (BI), build a reference system of business
intelligence application for enterprises. By analyzing
technology implementation and data logic of a real enterprise
IT planning scheme model, Hubei provincial branch of
China National Tobacco Corporation (CNTC) BI planning, a
feasible enterprise business intelligence design model is put
forward in this article. [56]
Office SharePoint Server 2007 features for business
intelligence, data integration features of Office SharePoint
Server 2007, and describes information presentation and
reporting features of SharePoint Server 2007.[58]
The relate components of a business intelligence system
gives a complete Business intelligence solution with
Microsoft SQL Server 2005. [57]
How to deliver BI solution with BI stack is used by
Microsoft business intelligence stack and BI products.[59]
By drawing on case law from analogous statutes to offer
a test that courts could use to define the mens rea of the
foreign benefit element in a way that limits the reach of the
law while respecting the text of the statute. [8]
The utilization of competitive intelligence tools for
effective strategic planning in higher education in the U.S
and introduces a more marketplace and corporate mind-set
into a setting driven by academic values and nonprofit
culture. [4]
Many dimensions of business model innovation,
focusing particularly on the relationship between a company
and its customers, and the methods that companies use to
grasp the bigger picture, or whole system perspective, that
enables them to understand how their enterprise relates to
the larger industry and broader economy in which it
operates.[32]
Business intelligence in advance exploiting an adaptive
approach. The idea is to learn business strategy once new
negotiation model rise in the e-market arena. It is used open
source software that implements a fully distributed open
environment for business negotiation.[1]
Specific case of business intelligence (BI)
infrastructures, should be decided according to the speed of
the decision-making processes, which are usually executed
in real time. It is determine the flexibility rate at which the
business can grow. Businesses grow but the key drivers can
remain the same. It analyzes the elements required for an
optimal deployment of smart decision architectures. [60]
An overview of the applied business intelligence methods
with regard to the utilization of the information and data
necessary for further analyses. Covering the period from 1
January 2005 to 1 January 2007, the data on defects in all
model ranges of the modern air-conditioned passenger
carriages were collected, processed and analyzed by
applying different methods. Based on the results of the
analysis, the most important causes of defects in the air-
conditioned carriages were identified.[28]
The implementation of business rules, as an essential part
in the development of BI systems, proper for the actual
business climate and its underlying fluctuations. Business
Intelligence (BI) is one of the instruments that offer support
in getting beyond crisis. If properly developed and
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implemented, BI can lead to improvements in decision
making and to operational efficiency. [29]
The state-of-art concept of process-oriented business
intelligence and analyzes its application architecture in
manufacturing enterprises from the organizational aspect.
An application case in manufacturing process is put
forward to illuminate function and benefits of process-
oriented business intelligence for manufacturing process
users.[6]
The Internet changed the trading game by making
market information instantly available to many more
people, spawning a large population of day traders,
bloggers, and market speculators. Information generation
and analysis, long the province of well-funded, large
financial institutions, has become fair game for all, even
people with limited means, from college students to
retirees. [11]
Mobile business intelligence tool (MBIT) aims to
provide these features in a flexible and cost-efficient
manner. It describes the detailed architecture of MBIT to
overcome the limitations of existing mobile business
intelligence tools. It discuss the benefits of using service
oriented architecture to design flexible and platform
independent mobile business applications.[42]
Enterprise adoption of open source business
intelligence (BI) is on the upswing, even in use cases
where the solution is embedded into a mission-critical
application. This paper will offer some key “do” and
“don’t” tips to help the reader avoid common mistakes or
missteps. [35]
III. INTEGRATED APPROACH
A.Integrated between BI,Supply Chain
3,33 % papers discuss about integrated between BI and
Supply Chain Management.
Business Intelligence, the basic technology of Business
Intelligence, and the contents of Supply Chain Integration
and focuses on the analysis of the application of Business
Intelligence in Supply Chain Integration to provide basis for
enterprises to implement Business Intelligence.[24]
Supply Chain Business Intelligence introduces driving
forces for its adoption and describes the supply chain BI
architecture. The global supply chain performance
measurement system based on the process reference model is
described. The main cutting-edge technologies such as
service-oriented architecture (SOA), business activity
monitoring (BAM), web portals, data mining, and their role
in BI systems are also discussed. Finally, key BI trends and
technologies that will influence future systems are
described.[45]
B.Integrated between BI, CRM System
6,67 % papers discuss about integrated between BI and
Customer Relationship Management System.
CRM systems and Business Intelligence provides a
holistic approach to customers which includes improvements
in customer profiling, simpler detection value for customers,
measuring the success of the company in satisfying its
customers, and create a comprehensive customer relationship
management. [13]
A conceptual and a technological infrastructure was
proposed and integrated into a Student Relationship
Management (SRM) system associated with Business
Intelligence concepts and technologies used to obtain
knowledge about the students and to support the decision
making process. [37]
In an in depth study of organizations across North
America and Europe, IDC found the average return on an
investment in business analytics was 431%. While more than
60% of organizations surveyed by IDC said they would
spend part of their budgets on BI in the next 12 months.
Maybe BI can take a page out of the CRM book when it
comes to marketing and scale down solutions to meet the
needs of companies that don't have the deep pockets of the
financial services industry.[24]
E-business intelligence aims to develop a tremendous
spectrum of business opportunities and user's adoption of the
business intelligence is very important and relevant
propositions are made.[52]
C. Integrated between BI, Data Mining
5 % papers discuss about integrated between BI and
Data Mining.
A data mining methodology called Business Intelligence-
driven Data Mining (BIdDM) combines knowledge-driven
data mining and method-driven data mining, and fills the gap
between business intelligence knowledge and existent
various data mining methods in e-Business. BIdDM contains
two processes: a construction process of a four-layer
framework and a data mining process. A methodology is
established in setting up the four-layer framework, which is
an important part in BIdDM. A case study of B2C e-Shop is
provided to illustrate the use of BIdDM. [55]
Business intelligence is information about a company's
past performance that is used to help predict the company's
future performance. It can reveal emerging trends from
which the company might profit. Data mining allows users
to sift through the enormous amount of information
available in data warehouses; it is from this sifting process
that business intelligence gems may be found. [40]
The business intelligence explorer did optimize the
search result or not, this paper chose three research objects,
Google, Quintura, Clusty, and conducted an analysis of
variance in terms of efficiency, effectiveness and usability.
The result shows that visualization and clustering techniques
offers practical implications for search engine users. [51]
D. Integrated between BI, AI (Artificial Intelligence)
3,33 % papers discuss about integrated between BI and
Artificial Intelligence.
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A business intelligence application of neural networks
in analyzing consumer heterogeneity in the context of
eating-out behavior in Taiwan. The data set for this study
has been collected through a survey of 800 Taiwanese
consumers. The results of our data analysis show that the
neural network rule extraction algorithm is able to find
distinct consumer segments and predict the consumers
within each segment with good accuracy. [14]
A hybrid fuzzy-Delphi-AHP approach to propose a more
comprehensive framework with specific business elements,
and also points out six performance indices for firms to
adjust business strategy. In order to reduce business risk in
developing international markets, using the alliance model
is a key strategy for information service firms. On the other
hand, firms should handle more accurate business
information to support their business intelligence (BI)
system to make better business decisions. [30]
E. Integrated between BI and OLAP
3,33 % papers discuss about integrated between BI and
OLAP.
The use of business intelligence and OLAP tools in e-
learning environments and presents a case study of how to
apply these technologies in the database of an e-learning
system. The study shows that students spend little time with
course courseware and prefer to use collaborative activities,
such as virtual classroom and forums instead of just viewing
the learning material.[9]
The importance of Intelligence Systems as well as the
architecture of OLAP decisional interactive support systems.
[39]
F. Integrated between BI, Knowledge Management
1,67 % papers discuss about integrated between BI and
Knowledge Management.
The relation between business intelligence and
knowledge management is analyzed. The conceptual maps,
their objectives, components, construction failures, as well
as their main advantages for a significant learning are
defined as intelligence products. Finally, the execution of an
intelligence product is illustrated by using mapping and geo-
reference techniques aimed at facilitating a substantial
learning on the part of its users. [38]
G.Integrated between BI, Business Process Management
3,33 % papers discuss about integrated between BI and
Business Process Management
BPM implementation often combines financial with non-
financial metrics that can identify the health of an enterprise
from a variety of perspectives. BI and BPM applications
implement multidimensional models, powerful models for
data analysis and simulation. The present paper describes a
multidimensional model that supports the construction of the
master budget of an enterprise with simulation facilities.[43]
It is leverage the large data infrastructure investments
(e.g. ERP systems) made by firms, and have the potential to
realize the substantial value locked up in a firm's data
resources. Business investment in BI systems is continuing
to accelerate, there is a complete absence of a specific and
rigorous method to measure the realized business value, if
any. It is developed a new measure that is based on an
understanding of the characteristics of BI systems in a
process-oriented framework. [30]
H. Integrated between BI and Strategic Management
3,33 % papers discuss about integrated between BI and
OLAP.
How management of sustainability in organisations can
be supported by business intelligence (BI) systems. One
phase of any BI project, the information planning phase, i.e.,
the systematic way of defining relevant information in order
to integrate it in reporting activities. Using grounded theory,
the main contribution of this study is to propose a
conceptual model that seeks to support the process of
integration of socio-environmental indicators into
organizational strategy for sustainability [27]
Evidence suggests that some factors can determine the
successful implementation of strategic IT systems, i.e.
Business Intelligence operations they are painstakingly
difficult to implement. This paper identify some strategic
and tactical actions that Chinese CEOs can use to foster a
knowledge sharing culture that is conducive to BI systems
implementation.[44]
Business intelligence (BI) is a strategic approach for
systematically targeting, tracking, communicating and
transforming relevant weak signs into actionable
information on which strategic decision-making is based.
Despite the increasing importance of BI, there is little
underlying theoretical work, which directly can guide the
interpretation of ambiguous weak signs. It gives an insight
into the issue through a new strategic business intelligence
system called PUZZLE. It describe this system and validate
it by designing a prototype, test the system using in-depth
interviews, and hold learning sessions in order to further
knowledge about BI. [20]
I. Integrated between BI and ANP (Analytic Network
Process)
1,67 % papers discuss about integrated between BI and
ANP.
The electronization has enabled (BI) systems for the
purpose of decision-making. It is important to clarify the
impact factors of a BI system and find out a suitable
assessment method to evaluate the performance of BI
systems. An analytic network process (ANP) based
assessment model was constructed to assess the
effectiveness of BI systems. The results indicate that the
most critical factors that impact the effectiveness of a BI
system are: output information accuracy, conformity to the
requirements, and support of organizational efficiency. [53]
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I. Integrated between BI, Quality Management System
1,67 % papers discuss about integrated between BI,
Quality Management System.
Researches the application of Quality Management
Systems in ISO-9001:2000-standard-based Business
Intelligence Services. Some of the topics here in addressed
are as follows: concepts of Business Intelligence, its
services and products; ISO 9001:2000 Quality Management
Systems (QMS), their characteristics,
benefits/disadvantages; and the results of implementing an
ISO-9001:2000-standard-based QMS in a Center for
Business Intelligence Services. Also, there is an analysis of
the advantages and disadvantages generated by it for the
organization. [5]
J. Integrated between BI, CRM and Data Mining
As more retailers evolve into customer-centric and
segment-based business, business intelligence (BI) and
customer relationship management (CRM) systems are
playing a key role in achieving and maintaining competitive
advantage. When the first Fingerhut company peaked in
1998, as many as 200 analysts and 40 statisticians mined the
database for insights that helped predict consumer shopping
patterns and credit behaviour. Data mining and BI helped
Fingerhut spot shopping patterns, bring product offerings to
the right customers, and nurture customer relationships. [36]
K. Integrated between BI, DSS (Decision Support System),
Performance Scorecard
1,67 % papers discuss about integrated between BI and
Decision Support System, Performance Scorecard.
Office of Higher Education Commission uses Microsoft
SQL Server 2005 Business Intelligence Enterprise Data
Integration Tool to develop OHEC DSS and develop a web
application to develop the Executive Decision Support
System (DSS). It is developed a Performance Scorecard,
interactive and Business Insight Report after making BI [21]
L.Integrated between BI, AI, and Data Mining
5 % papers discuss about integrated between BI
(Business Intelligence), AI (Artificial Intelligence) and Data
Mining
The evolution of BI is divided into 3 stages: The
existence of a business information system that covers the
operational activities of the business and operational data,
historical data has been separated from operational data into
data warehouse designed to store and access data quickly,
BI systems currently involve data mining techniques and
artificial intelligence in the extract knowledge for decision
making.[20]
This papers discuss about efficient data mining tools and
presents an intelligent BI system framework based on many
computational intelligence paradigms, including a predictor
tool based on neuro-computing (cerebellar model articulation
controller neural network, CMAC NN), a classifier tool
based on neuro-computing (CMAC NN) and optimizer tools
based on evolutionary computing and artificial life (such as
real-coded genetic algorithm and artificial immune system).
[17]
Information about the benefits using Commercial Off-
the-Shelf (COTS) business intelligence software tools to
support aircraft and automated test system maintenance
environments. By using these engineering cluster models
produced earlier to develop and build more accurate
predictive models, predictive algorithms are utilized to
make use of the cluster results to improve predictive
accuracy. Common industry business intelligence Decision
Trees and Neural Network models are developed to
determine which algorithm produces the most
accurate models (as measured by comparing predictions
with actual values over the testing set). After an initial
mining structure and mining model is built (specifying the
input and predictable attributes),the analyst can easily add
other mining models. [12]
M. Integrated between BI,Data Mining, Knowledge
Management
1,67 % papers discuss about integrated between BI, Data
Mining and Knowledge Management
A novel model employing knowledge management in
data mining process to reduce data, analysis and action
latency of real-time business intelligence. [15]
N. Integrated between BI, Business Process Management
(BPM), Knowledge Management (KM)
1,67% papers discuss about integrated between BI, BPM
and Knowledge Management
That further opportunities for business value creation
could be discovered through systematic analysis of the non-
technical aspects of BI and BPM integration, especially in
terms of strategy alignment, human-centered knowledge
management and ongoing improvement of BI supported
processes. The paper proposes a theoretical framework
founded in the related research in BPM, BI and Knowledge
Management (KM) fields and describes how it has been
used to guide our empirical case study research in service
organizations in the context of BI-supported customer-
facing processes. [25]
O. Integrated between BI,CRM, SCM dan ERP
1,67 % papers discuss about integrated between BI,
CRM, SCM and ERP
Methods of raising corporation's decision-making ability
which based on Web service are introduced in this paper.
Several research results are also introduced here: the
application of some business software, such as Enterprise
Resource Planning, Customer Resources Management,
Supply Chain Management and so on; the corporation's
effective analysis of data; methods of building Business
Intelligence network. Cooperation of business intelligence
system and share of knowledge will be realized between
corporations. [52]
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P. Integrated between BI, ETL and OLAP
3,33 % papers discuss about integrated between BI,
ETL, OLAP
Business intelligence (BI) tools to take the mechanics
out of the process. Gartner's leading BI analysts highlighted
several major flaws: 1. Too many IT departments build a
data warehouse on the assumption that once it is built, users
will automatically see the benefit. 2. Reliance on
spreadsheets. 3. Data quality. The BI world is full of
technical terms, such as extract, transform & load (ETL) and
data warehouses. This may explain why the technology has
not done well in organizations with no IT department. First,
data must be extracted, usually from multiple sources, and
transformed (cleaned up) for consistency and accuracy.
Then it is loaded into a data warehouse that stores the data
in a logical way. ETL can account for 50% of the total cost
of a BI implementation. With an OLAP cube, you can
interactively slice and dice the data across multiple
dimensions and drill down for more detail.[26]
A process oriented to the addition of business
intelligence (BI) elements at Universidad de Tarapacá
(UTA), Arica, Chile. For the purpose, a data mart (DM) was
implemented, focused on the Admission and Registration
area of Academic Vice-Rectory. Its development required
carrying out activities such as to obtain business
requirements, to investigate the area key performance
indicator (KPI), to analyze several internal information
sources and to develop a dimensional model based on the
Kimball star schema. For proper implementation and
integration of these data repositories, extraction,
transformation and loading (ETL) processes were carried
out from two data sources. The creation of this DM, allowed
users of the Academic Vice-Rectory to visualize the
information they required through online analytical
processing (OLAP) tools. [10]
Q. Integrated between BI, Data Mining, Decision Support
System, Strategic Management
1,67 % papers discuss about integrated between BI, Data
Mining, Decision Support System, Strategic Management.
A Business Intelligence process for ISP dealers in Taiwan
to assist management in developing effective service
management strategies. It is explored the customers’ usage
characteristics and preference knowledge through applying
the attribute-oriented induction (AOI) method on IP traffic
data of users. Using the self-organizing map (SOM) method,
it is divided customers into clusters with different usage
behavior patterns. It is apply RFM modeling to calibrate
customers’ value of each cluster, which will enable the
management to develop direct and effective marketing
strategies. With actual data from one major ISP, it is
develop a BI decision support system with visual
presentation, which is well received by its management
staff.[23]
R. Integrated between BI, CRM and AI
1,67 % papers discuss about integrated between BI,
CRM (Customer Relationship Management) and AI
(Artificial Intelligence).
Many CRM researches have been performed to calculate
customer profitability and develop a comprehensive model
of it. This paper aims at providing an easy, efficient and
more practical alternative approach based on the customer
satisfaction survey for the profitable customers
segmentation. A multi-agent-based system, called the
survey-based profitable customers segmentation system that
executes the customer satisfaction survey and conducts the
mining of customer satisfaction survey, socio-demographic
and accounting database through the integrated uses of
business intelligence tools such as DEA (Data Envelopment
Analysis), Self-Organizing Map (SOM) neural network and
C4.5 for the profitable customers segmentation. A case
study on a Motor company's profitable customer
segmentation is illustrated.[17].
IV. OBSERVATION AND RECOMMENDATION
A. The most popular approach
The most popular approach is single approach Business
Intelligence System with 46,67 % of paper discuss it.
They discuss about the theoretic, method, model,
architecture, tools, system and case study of implementation
of Business Intelligence.
There are Business Intelligence from Voice of
Customer. There are Even Driven Architecture, process
oriented and service oriented architecture.
There are many software that is used in Business
Intelligence System research like SharePoint Server 2007,
Microsoft SQL Server 2005, Microsoft business intelligence
stack and BI products, and finally, describes how to deliver
BI solution with BI stack, and open source Business
Intelligence.
B. The most popular BI integrated research
The most popular BI integrated research is Integrated
between Business Intelligence and CRM System with 6,67
% papers. Integrated between BI, Data Mining 5 % and
Integrated between BI, AI and, Data Mining 5 %
The topic that integrated with BI research that is found in
this research is Supply Chain Management, Customer
Relationship Management, Data Mining, Data Warehouse,
Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management,
Artificial Intelligence, Enterprise Resource Planning,
Extract Transformation Loading, OLAP, Quality
Management System.
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Table 1. Topic that integrated with
Business Intelligence System
NO TOPIC SUM PERCENTAGE
1 Business Intelligence System 28 46.67%
2
Integrated between
BI,Supply Chain 2 3.33%
3
Integrated between BI, CRM
System 4 6.67%
4
Integrated between BI, Data
Mining 3 5.00%
5
Integrated between BI, AI
(Artificial Intelligence) 2 3.33%
6
Integrated between BI and
OLAP 2 3.33%
7
Integrated between BI,
Knowledge Management 1 1.67%
8
Integrated between BI and
BPM 2 3.33%
9
Integrated between BI and
Strategic Management 2 3.33%
10
Integrated between BI and
ANP 1 1.67%
11
Integrated between BI,
Quality Management System 1 1.67%
12
Integrated between BI,CRM,
Data Mining 1 1.67%
13
Integrated between BI, DSS,
Performance Scorecard 1 1.67%
14
Integrated between BI,AI
and,Data Mining 3 5.00%
15
Integrated BI,Data Mining,
Knowledge Management 1 1.67%
16
Integrated between BI,
Business Process
Management,KM 1 1.67%
17
Integrated between BI,CRM,
SCM dan ERP 1 1.67%
18
Integrated between BI, ETL
and OLAP 2 3.33%
19
Integrated between BI, Data
Mining,DSS,Strategy
Management 1 1.67%
20
Integrated between BI, CRM
and AI 1 1.67%
60 100.00%
C. Limitation of approaches
Limitation of approaches is sum of papers and the topic
that related to BI research. The topic that integrated with BI
research is in this research is Supply Chain Management,
Customer Relationship Management, Data Mining, Data
Warehouse, Decision Support System, Performance
Scorecard, Knowledge Management, Business Process
Management, Artificial Intelligence, Enterprise Resource
Planning, Extract Transformation Language, OLAP, Quality
Management System and Strategic Management
V. FUTURE WORK
The study was conducted to Milk Agro industry
scale Medium Enterprises in Indonesia. The systems
approach combined with the design of BI systems which
consists of 4 stages and 12 steps to obtain the BI system
prototype. The 4 stage is Analyze, Design, Planning,
Implementation and Controlling. The 12 steps is Need
Analysis; Problem Formulation; Business Need
Identification; Infrastructure Introduction; Planning the BI
Project; Identification Data, Data Warehouse, Data Mart
Definition; The definition of mathematical model needed;
Data Warehouse and Data Mart Development; Meta Data
Repository Development; ETL Development; Application
Development; Validation and Verification.
The research will integrated between BI, Data
Mining, Data Warehouse, OLAP, Artificial Intelligence,
Business Process Management and BI Scorecard.
Implementation and Controlling
Analize
Planning
Design
Agroindustry
Consumer
Supplier
Start
Need Analysis
Problem Formulation
Competitor
Government
Business
Environment
Business Need Identification
Infrastructure Introduction
Planning the BI Project
The definition of
mathematical
models needed
Project Need Breakdown
Identification Data, data
warehouse and data mart
definition
Prototype Development
Data Warehouse and Data Mart
Development
Meta Data Repository
Development
Application Development ETL Development
Validation and Verification
Suitable
Finish
Yes
No
BI System Model for Milk
Agro Industry
ETL: Extract, Transformation and Loading
Figure 1 Framework for Research Design Business
Intelligence System
VI. CONCLUSION
This paper reviews is based on a literature review on
business intelligence approaches the 60 journals of business
intelligence system. Relates articles appearing in the
international journal like Proquest, Ebscohost, Emerald,
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118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
Science Direct and IEEE Conference from 2000 to 2011 are
gathered
It was found 46,67 % research is in single approach
Business Intelligence System. Integrated between Business
Intelligence and Customer Relationship Management is the
most popular evaluating criteria with 6,67 %. Integrated
between BI, Data Mining 5 % and Integrated between BI,
AI and, Data Mining 5 %
The topic that integrated with BI research that is found in
this research is Supply Chain Management, Customer
Relationship Management, Data Mining, Data Warehouse,
Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management,
Artificial Intelligence, Enterprise Resource Planning,
Extract Transformation Loading, OLAP, Quality
Management System.
ACKNOWLEDGMENT
The first author thank to the Department of
Industrial Engineering, Trisakti University Jakarta Indonesia
for funding this research.
REFERENCES
1. Aciar, S. 2009. Adaptive business intelligence for an
open negotiation environment.Digital Ecosystems and
Technologies, 2009. DEST '09. 3rd IEEE International
Conference on Digital Object Identifier. Page(s): 517 –
522 IEEE Conferences
2. Al-Natsheh, H.T. 2010. Commercializing
computational intelligence techniques in a business
intelligence application; Congress Evolutionary
Computation (CEC), IEEE Congress on Digital Object
Identifier. Page(s): 1 – 7 IEEE Conferences
3. Barrento, M.P.A et al. 2010. Business intelligence
applied to Homogeneous Diagnostic Groups.
Information Systems and Technologies (CISTI), 5th
Iberian Conference, Page(s): 1 – 5 IEEE Conferences.
4. Barrett, Susan E. 2010. Competitive Intelligence:
Significance In Higher Education. Vol. 2 Issue 4. P26-
30. 5p.
5. Cartaya, Juan Carlos Carro. 2008. La inteligencia
empresarial y el Sistema de Gestión de Calidad ISO
9001:2000. Vol. 39 Issue 1. p31-44. 14p.
6. Cheng Yuan et al. 2010. The Research & Application
Of Process-Oriented Business Intelligence In
Manufacturing Industry Management And Service
Science (MASS), International Conference On Digital
Object Identifier. Page(S): 1 – 4 IEEE Conferences.
7. Chan Gaik Yee et al. 2010. Applying Instant Business
Intelligence In Marketing Campaign Automation.
Computer Research And Development, 2010 Second
International Conference On Digital Object Identifier,
Page(S): 643 – 646 IEEE Conferences
8. Edelman,Willian J. 2011.The "Benefit" Of Spying:
Defining The Boundaries Of Economic Espionage
Under The Economic Espionage Act Of 1996. Stanford
Law Review. Vol. 63 Issue 2, P447-474, 28p
9. Falakmasir M.H.et al.2010. Business intelligence in e-
learning: (case study on the Iran university of science
and technology dataset. Software Engineering and Data
Mining (SEDM). 2nd International Conference.Page(s):
473 – 477. IEEE Conferences
10. Fuentes Tapia, Louis. 2010. Incorporation Of Business
Intelligence Elements In The Admission And
Registration Process Of A Chilean University.
INGENIARE - Revista Chilena de Ingeniería. Vol. 18
Issue 3, P383-394, 12p.
11. Hsinchun Chen.2010. Business and Market Intelligence
2.0, Part 2. Volume: 25 , Issue: 2 Digital Object
Identifier. Page(s): 74 – 82 IEEE Journals.
12. Head, S.C et al. 2010.Using commercial off-the-shelf
business intelligence software tools to support aircraft
and automated test system maintenance environments.
IEEE Digital Object Identifier. Page(s): 1 – 6 IEEE
Conferences.
13. Habul, A. 2010. Business intelligence and customer
relationship management.; Information Technology
Interfaces (ITI), 32nd International Conference on,
Page(s): 169 – 174 IEEE Conferences.
14. Hayashi, Yoichi. 2010. Understanding consumer
heterogeneity: A business intelligence application of
neural networks. Knowledge-Based Systems. Vol. 23
Issue 8, p856-863, 8p
15. Houxing You.2010. A Knowledge Management
Approach for Real-Time Business Intelligence;a
Intelligent Systems and Applications (ISA), 2nd
International Workshop on Digital Object
Identifier.Page(s): 1 – 4 IEEE Conferences.
16. Jang Hee Lee, Sang Chan Park.2005. Intelligent
profitable customers segmentation system based on
business intelligence tools. Expert Systems with
Applications. Volume 29. Issue 1. Pages 145-152.
17. Jie Huang. 2010. Research on Mechanism and
Applicable Framework of E-Business Intelligence. E-
Business and E-Government (ICEE). 2010 International
Conference on Digital Object Identifier. Page(s): 195 –
198 IEEE Conferences.
18. Jui-Yu Wu. 2010. Computational Intelligence-Based
Intelligent Business Intelligence System: Concept and
Framework. Computer and Network Technology
(ICCNT). Second International Conference on Digital
Object Identifier, Page(s): 334 - 338 IEEE Conferences
19. Jun He et al. 2010. Research on EDA based Right-Time
Business Intelligence System. Information Management
and Engineering (ICIME), The 2nd IEEE International
Conference on Digital Object Identifier. Page(s): 476 –
479. IEEE Conferences
20. Kamel Rouibah et al. 2002. PUZZLE: a concept and
prototype for linking business intelligence to business
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 104
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
strategy. The Journal of Strategic Information Systems.
Volume 11. Issue 2. 1 June 2002. Pages 133-152.http://www.sciencedirect.com/science/article/pii/S0963
868702000057
21. Ko, I.S and Abdullaev, S.R. 2007. A Study on the
Aspects of Successful Business intelligence System
Development in Y.Shi et al. (Eds.):ICCS 2007.Part 4.
LNCS 4490. Pp.729-732. Spriger-Verlag Berlin
Heidenberg
22. Kleesuwan S, et all. 2010. Business Intelligence in
Thailand’s Higher Educational Resources Management.
Procedia Social and Behavioral Sciences 2.84-87. 2
23. Li, Sheng-Tun. 2008. Business Intelligence. Expert
Systems with Applications. Volume 35. Pages 739-754http://www.sciencedirect.com/science
24. Liu Luhao. 2010. Supply Chain Integration through
Business Intelligence; Management and Service
Science (MASS), International Conference on Digital
Object Identifier. Publication Year: 2010 , Page(s): 1 –
4 IEEE Conferences
25. Marjanovic, O. 2010. Business Value Creation through
Business Processes Management and Operational
Business Intelligence Integration a System Sciences
(HICSS), 43rd Hawaii International Conference on
Digital Object Identifier. Page(s): 1 – 10 IEEE
Conferences.
26. Maira Petrini et al.2009. Managing sustainability with
the support of business intelligence: Integrating socio-
environmental indicators and organisational
context. The Journal of Strategic Information Systems,
Volume 18, Issue 4, December 2009, Pages 178-191
27. Michael Burns.2005. Business intelligence survey. CA
Magazine.Toronto. Vol.138. Iss.5. pg 18.
28. Milkovi?, Vili et al.2009. An Analysis Of Device And
Equipment Failures By Means Of Business Intelligence
Methods. Transactions of FAMENA. Vol. 33 Issue 4.
p53-62. 10p.
29. Mircea, Marinela et al. 2009. Using Business Rules In
Business Intelligence. Journal of Applied Qualitative
Methods. Vol. 4 Issue 3, p382-393, 12p
30. Ming-Kuen Chen, Shih-Ching Wang.
.
2010.
The use of
a hybrid fuzzy-Delphi-AHP approach to develop global
business intelligence for information service firms.http://www.sciencedirect.com/science
31. Mohamed Z. Elbashir.2008. Measuring the effects of
business intelligence systems: The relationship between
business process and organizational performance.
International Journal of Accounting Information
Systems. Volume 9, Issue 3, September 2008, Pages
135-153. Eighth International Research Symposium on
Accounting Information Systems (IRSAIS)
32. Morris, Langdon. 2009.Business Model Innovation The
Strategy of Business Breakthroughs. International
Journal of Innovation Science. Vol. 1 Issue 4. p191-
204.14p
33. Najmi, Manoochehr et al. 2010. The evaluation of
Business Intelligence maturity level in Iranian banking
industry. 17
Th
International Conference Industrial
Engineering and Engineering Management (ICIEEM)
on Digital Object Identifier..Page(s): 466 - 470. IEEE
Conferences.
34. Neil Sutton. 2004. Bullish on business intelligence.
Computing Canada. Willowdale. Vol. 30
Iss. 14. pg. 24. 1 pgshttp://proquest.umi.com/pqdweb?did=723745941&sid=
2&Fmt=3&clientId=60726&RQT=309&VName=PQD
35. Nick Halsey. 2006. Enterprise Open Source BI in
Mission-Critical Applications: Dos and Don’ts.
Business Intelligence Journal . Vol. 14, No. 4
36. Phan, Dien D et al. 2010. A model of customer
relationship management and business intelligence
systems for catalogue and online retailers. December
2010, Pages 559-566. Elsevier B.V.
37. Piedade, M.B et a.2010. Business intelligence in higher
education: Enhancing the teaching-learning process
with a SRM system. Information Systems and
Technologies (CISTI), 5th Iberian Conference. Page(s):
1 – 5 IEEE Conferences.
38. Pina, Ramon Antonio Rodríguez et al.2008. Conceptual
maps and geo-references in business intelligence
products and services. ACIMED. Vol. 17 Issue 4. p91-
104. 14p.
39. Pirnau, M et al. 2010. General information on business
Intelligence and OLAP systems architecture.; Computer
and Automation Engineering (ICCAE), The 2nd
International Conference on Volume: 2 Digital Object
Identifier. Page(s): 294 – 297 IEEE Conferences.
40. Pillai, Jyothi.2011. User centric approach to itemset
utility mining in Market Basket Analysis. International
Journal on Computer Science & Engineering. Vol. 3
Issue 1. p393-400. 8p
41. Rodriguez, C.et al 2010. Internet Computing. IEEE
Volume: 14. 4 Digital Object Identifier , Page(s): 32 –
40. IEEE Journals
42. Sajjad, B. 2010. An open source service oriented
Mobile Business Intelligence Tool (MBIT).
Information and Communica tion Technologies, 2009.
ICICT '09. International Conference on Digital Object
Identifier: a Publication Year: 2009 , Page(s): 235 –
240 IEEE Conferences.
43. Sandu, Daniela Ioana. 2009. Multidimensional Model
For The Master Budget. Journal of Applied
Quantitative Methods. Vol. 4. Issue 4. p408-421, 14p
44. Seah Melody,et al.2010. A case analysis of Savecom:
The role of indigenous leadership in implementing a
business intelligence system. Pages 368-373. Elsevier
Ltd.http://www.sciencedirect.com/science/journal/
02684012
45. Stefanovic, N. and Stefanovic, D. 2009. Supply Chain
Business Intelligence: Technologies, Issues and Trends.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 105
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
in M. Bramer(Ed.): Artificial Intelligence. LNAI 5640.
IFIP International Federation for Information
Processing.
46. Subramaniam, L.V et al. 2009. Business Intelligence
from Voice of Customer. International Conference Data
Engineering, ICDE '09. IEEE 25th International
Conference on Digital Object Identifier. Page(s): 1391
– 1402 IEEE Conferences.
47. Xu Xi et al. 2010. Developing a Framework for
Business Intelligence Systems Integration Based on
Ontology.. ICNDS '09. International Conference
Networking and Digital Society on Volume: 2 Digital
Object Identifier. Page(s): 288 – 291 IEEE Conferences
48. Yeoh, William and Koronios, Andy.2010. Critical
Success Factor for Business Intelligence System.
Journal for Information System. Spring.
49. Yong Feng. et al. 2010 Intelligence System Based on
Multi-agent Design of the Low-Cost Business
Information Science and Management Engineering
(ISME), International Conference of Volume: 1 Digital
Object Identifier.
50. Yu, Zhaohui et al. 2010. The comparative study of the
Business Intelligence Explorer and the traditional.
Biomedical Engineering and Informatics (BMEI), 2010
3rd International Conference on Volume: 7 Digital
Object Identifier: a Publication Year: 2010 , Page(s):
2985 – 2989 IEEE Conferences
51. Yuantao Jiang. 2009. A conceptual framework and
hypotheses for the adoption of e-business intelligence.
Computing, Communication, Control, and
Management, 2009. CCCM 2009. ISECS International
Colloquium on Volume: 4 Digital Object Identifier.
Publication Year: 2009 , Page(s): 558 – 561 IEEE
Conferences.
52. Yujun Bao et al. 2009. Research of Business
Intelligence Which Based Upon Web; E-Business and
Information System Security. EBISS '09. International
Conference on Digital Object Identifier. Page(s): 1 – 4
IEEE Conferences.
53. Yu-Hsin Lin et al. 2009. Research on using ANP to
establish a performance assessment model for business
intelligence systems. March 2009, Pages 4135-4146http://www.sciencedirect.com/science
54. Yang Hang et al. 2009. A Framework of Business
Intelligence-Driven Data Mining for E-business; NCM
'09. Fifth International Joint Conference on Digital
Object Identifier. Page(s): 1964 – 1970 IEEE
Conferences.
55. Zhang Hai. 2009. Research Automated Negotiation
Framework for Business Intelligence Systems.
International Conference Networking and Digital
Society.. ICNDS '09. Digital Object Identifier. .
Page(s): 292 – 295 IEEE Conferences.
56. Zhang, Liyi. 2009. A Feasible Enterprise Business
Intelligence Design Model.a Management of e-
Commerce and e-Government, 2009. ICMECG '09.
International Conference on Digital Object Identifier:
Page(s): 182 - 187IEEE Conferences
57. Zhijun Ren. 2010. Constructing a Business Intelligence
Solution with Microsoft SQL Server 2005. Biomedical
Engineering and Computer Science (ICBECS),
International Conference on Digital Object Identifier,
Page(s): 1 – 4 IEEE Conferences.
58. Zhijun Ren. 2010. Constructing Business Intelligence
Solution with Share. Point Server 2007. International
Conference on Digital Object Identifier. Page(s): 615 –
618 IEEE Conferences.
59. Zhijun Ren et al. 2010. Delivering a Comprehensive BI
Solution with Microsoft Business Intelligence, a
Challenges in Environmental Science and Computer
Engineering (CESCE), International Conference
Volume: 2 Digital Object Identifier, Page(s): 278 – 281
IEEE Conferences.
60. Zuluaga, Givanni Gomez. 2011. Smart Decision
Infrastructure: Architecture Discussion.Cybernetics &
Systems. Vol. 42 Issue 2. p139-155, 17p
doc_280201885.pdf
Progress in Business Intelligence System research
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 96
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I J E N S
Progress in Business Intelligence System research : A literature Review
Rina Fitriana
1
, Eriyatno
2
, Taufik Djatna
3
1
Department of Industrial Engineering
Trisakti University
Jakarta Indonesia
[email protected]
2,3
Dept of Agro-Industrial Technology
Bogor Agriculture University
Bogor ,West Java, Indonesia
[email protected], [email protected]
Abstract—This paper reviews the literature of Progress in Business Intelligence System research. Relates articles
appearing in the international journal like Proquest, Ebscohost, Emerald, Science Direct and IEEE Conference from 2000 to
2011 are gathered and analyzed so the following three question can be answered
1)Which approached were mostpupular?(ii)Which the most popular BI integrated research. It was found 50% research is in single approach Business
Intelligence System. Integrated research between Business Intelligence and Data Mining is the most popular evaluating
criteria with 6,67 %. The topic that integrated with BI research that is found in this research is Supply Chain Management,
Customer Relationship Management, Data Mining, Data Warehouse, Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management, Artificial Intelligence, Enterprise Resource Planning, Extract
Transformation Loading, OLAP, Quality Management System, Strategic Management.
Keywords: business intelligence, data mining, data warehouse, decision support system, supply chain management, artificial
intelligence, quality management system
I. INTRODUCTION
Business Intelligence is a process for extracting,
transforming, managing and analyzing large data by make a
mathematical model to gain information and knowledge to
help make decisions in the complex. Elements of Business
Intelligence are Data Warehouse, Data Mining and Decision
Support System. There are many integrated topic that is
integrated in Business Intelligence System research.
The general objective of this research is to make
literature review of Progress in Business Intelligence System
research. This paper reviews the 60 journals of business
intelligence. Relates articles appearing in the international
journal like Proquest, Ebscohost, Emerald, Science Direct
and IEEE Conference from 2000 to 2011 are gathered
The paper is organized as follows: Section 2 dan 3
describe the individual approaches and integrated approaches
critically respective. Section 4 analysis the most prevalently
used approaches discuss the most popular integrated research
of Business Intelligence. Section 5 suggested for future
work. Section 6 concludes the paper.
II. INDIVIDUAL APPROACH
A. Business Intelligence System
46,67 % papers discuss about individual approach the
theoretic, method, software of business intelligence system.
The papers writes the definition, methodology, architecture,
case study, software that used in business intelligence
system.
Manufacturing resource management system (MRMS)
analyzes the current situation of business environment and
business intelligence systems framework at first, and studies
the theoretic and methods about the business intelligence
system and analyzes the necessity of an automated
negotiation method in the based on the manufacture
requirement and latency manufacturing resource state, order
enterprise can find forwardly or passively some
manufacturers satisfying it manufacturing tasks requirement
taking into account justice, multi-topics influence genes, and
negotiation both sides preference, etc. [55]
Evolutional objectives of BI system in E-business,
explores the application way of BI and the working
mechanism of BI system in E-business ,and more expounds
the its operational framework of E-business intelligence
system. [17]
CMMI (Capability Maturity Model Integrated) and
makes a contribution on the empirical knowledge on CMMI.
CMMI is developed to define different levels of software
process maturity. The concepts underlying CMMI have been
defined different maturity levels for a business intelligence
process.[33]
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The critical success factor in business intelligence
system success seeks to bridge the gap that exist between
academia and practitioner by investigating.[48]
The current applied status of business intelligence and
multi-agent technology and design of the low-cost business
intelligence system based on multi-agent is put forward,
which is composed of the low-cost business intelligence
system framework, the analysis of the core components'
function and the operation mechanism of the system.[50]
Even-Driven Architecture (EDA) based Right-Time
Business Intelligence System Framework (EDA based RT-
BISF), which combines RT-BI and the business process
based on the EDA and Agent, to resolve the environment
uncertainty, business dynamics and to meet the needs of
dynamic adjustment of business solution for the enterprise in
the fierce competitive environment.[19]
An Enterprise Marketing Campaign Automation
(EMCA) system that can provide data for businesses to
instantly assemble them for determining effective and
accurate marketing campaign strategy. By generating a
mailing list targeted to a specific group of buyers with
reference to their buying habits can reduce marketing cost by
just mailing the promotional items to the specific group of
buyers. [7]
A Business Intelligence (BI) development project applied
to Homogeneous Diagnostic Groups (GDH) which are very
specific and important for health management. The main
goal of this project was make available data in a simple way
for end users to have a support decision tool that increases
the performance of decision making. [3]
How these scenarios impact information quality in
business intelligence applications and lead to nontrivial
research challenges. They describe the idea of uncertain
events and key indicators and present a model to express and
store uncertainty and a tool to compute and visualize
uncertain key indicators.[41]
The current situation of business environment and
business intelligence systems (BIS) framework at first, and
studies the theoretic and methods about the business
intelligence system based on ontology. Based on ontology,
this paper proposes an integration framework for business
intelligence systems. [47]
A first of a kind system, called business intelligence from
voice of customer (BIVoC), that can: 1) combine
unstructured information and structured information in an
information intensive enterprise and 2) derive richer business
insights from the combined data. [46]
The commercialization of a business intelligence
application deploying computational intelligence techniques.
Theoretical foundations are included where appropriate,
along with implementation and comparative benchmark
results. Discussions on technology transfer mechanisms are
included, identifying a generic framework for the
commercialization of technology innovations, with a
particular case study from Jordan .[2]
The application framework of enterprise business
intelligence (BI), build a reference system of business
intelligence application for enterprises. By analyzing
technology implementation and data logic of a real enterprise
IT planning scheme model, Hubei provincial branch of
China National Tobacco Corporation (CNTC) BI planning, a
feasible enterprise business intelligence design model is put
forward in this article. [56]
Office SharePoint Server 2007 features for business
intelligence, data integration features of Office SharePoint
Server 2007, and describes information presentation and
reporting features of SharePoint Server 2007.[58]
The relate components of a business intelligence system
gives a complete Business intelligence solution with
Microsoft SQL Server 2005. [57]
How to deliver BI solution with BI stack is used by
Microsoft business intelligence stack and BI products.[59]
By drawing on case law from analogous statutes to offer
a test that courts could use to define the mens rea of the
foreign benefit element in a way that limits the reach of the
law while respecting the text of the statute. [8]
The utilization of competitive intelligence tools for
effective strategic planning in higher education in the U.S
and introduces a more marketplace and corporate mind-set
into a setting driven by academic values and nonprofit
culture. [4]
Many dimensions of business model innovation,
focusing particularly on the relationship between a company
and its customers, and the methods that companies use to
grasp the bigger picture, or whole system perspective, that
enables them to understand how their enterprise relates to
the larger industry and broader economy in which it
operates.[32]
Business intelligence in advance exploiting an adaptive
approach. The idea is to learn business strategy once new
negotiation model rise in the e-market arena. It is used open
source software that implements a fully distributed open
environment for business negotiation.[1]
Specific case of business intelligence (BI)
infrastructures, should be decided according to the speed of
the decision-making processes, which are usually executed
in real time. It is determine the flexibility rate at which the
business can grow. Businesses grow but the key drivers can
remain the same. It analyzes the elements required for an
optimal deployment of smart decision architectures. [60]
An overview of the applied business intelligence methods
with regard to the utilization of the information and data
necessary for further analyses. Covering the period from 1
January 2005 to 1 January 2007, the data on defects in all
model ranges of the modern air-conditioned passenger
carriages were collected, processed and analyzed by
applying different methods. Based on the results of the
analysis, the most important causes of defects in the air-
conditioned carriages were identified.[28]
The implementation of business rules, as an essential part
in the development of BI systems, proper for the actual
business climate and its underlying fluctuations. Business
Intelligence (BI) is one of the instruments that offer support
in getting beyond crisis. If properly developed and
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implemented, BI can lead to improvements in decision
making and to operational efficiency. [29]
The state-of-art concept of process-oriented business
intelligence and analyzes its application architecture in
manufacturing enterprises from the organizational aspect.
An application case in manufacturing process is put
forward to illuminate function and benefits of process-
oriented business intelligence for manufacturing process
users.[6]
The Internet changed the trading game by making
market information instantly available to many more
people, spawning a large population of day traders,
bloggers, and market speculators. Information generation
and analysis, long the province of well-funded, large
financial institutions, has become fair game for all, even
people with limited means, from college students to
retirees. [11]
Mobile business intelligence tool (MBIT) aims to
provide these features in a flexible and cost-efficient
manner. It describes the detailed architecture of MBIT to
overcome the limitations of existing mobile business
intelligence tools. It discuss the benefits of using service
oriented architecture to design flexible and platform
independent mobile business applications.[42]
Enterprise adoption of open source business
intelligence (BI) is on the upswing, even in use cases
where the solution is embedded into a mission-critical
application. This paper will offer some key “do” and
“don’t” tips to help the reader avoid common mistakes or
missteps. [35]
III. INTEGRATED APPROACH
A.Integrated between BI,Supply Chain
3,33 % papers discuss about integrated between BI and
Supply Chain Management.
Business Intelligence, the basic technology of Business
Intelligence, and the contents of Supply Chain Integration
and focuses on the analysis of the application of Business
Intelligence in Supply Chain Integration to provide basis for
enterprises to implement Business Intelligence.[24]
Supply Chain Business Intelligence introduces driving
forces for its adoption and describes the supply chain BI
architecture. The global supply chain performance
measurement system based on the process reference model is
described. The main cutting-edge technologies such as
service-oriented architecture (SOA), business activity
monitoring (BAM), web portals, data mining, and their role
in BI systems are also discussed. Finally, key BI trends and
technologies that will influence future systems are
described.[45]
B.Integrated between BI, CRM System
6,67 % papers discuss about integrated between BI and
Customer Relationship Management System.
CRM systems and Business Intelligence provides a
holistic approach to customers which includes improvements
in customer profiling, simpler detection value for customers,
measuring the success of the company in satisfying its
customers, and create a comprehensive customer relationship
management. [13]
A conceptual and a technological infrastructure was
proposed and integrated into a Student Relationship
Management (SRM) system associated with Business
Intelligence concepts and technologies used to obtain
knowledge about the students and to support the decision
making process. [37]
In an in depth study of organizations across North
America and Europe, IDC found the average return on an
investment in business analytics was 431%. While more than
60% of organizations surveyed by IDC said they would
spend part of their budgets on BI in the next 12 months.
Maybe BI can take a page out of the CRM book when it
comes to marketing and scale down solutions to meet the
needs of companies that don't have the deep pockets of the
financial services industry.[24]
E-business intelligence aims to develop a tremendous
spectrum of business opportunities and user's adoption of the
business intelligence is very important and relevant
propositions are made.[52]
C. Integrated between BI, Data Mining
5 % papers discuss about integrated between BI and
Data Mining.
A data mining methodology called Business Intelligence-
driven Data Mining (BIdDM) combines knowledge-driven
data mining and method-driven data mining, and fills the gap
between business intelligence knowledge and existent
various data mining methods in e-Business. BIdDM contains
two processes: a construction process of a four-layer
framework and a data mining process. A methodology is
established in setting up the four-layer framework, which is
an important part in BIdDM. A case study of B2C e-Shop is
provided to illustrate the use of BIdDM. [55]
Business intelligence is information about a company's
past performance that is used to help predict the company's
future performance. It can reveal emerging trends from
which the company might profit. Data mining allows users
to sift through the enormous amount of information
available in data warehouses; it is from this sifting process
that business intelligence gems may be found. [40]
The business intelligence explorer did optimize the
search result or not, this paper chose three research objects,
Google, Quintura, Clusty, and conducted an analysis of
variance in terms of efficiency, effectiveness and usability.
The result shows that visualization and clustering techniques
offers practical implications for search engine users. [51]
D. Integrated between BI, AI (Artificial Intelligence)
3,33 % papers discuss about integrated between BI and
Artificial Intelligence.
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A business intelligence application of neural networks
in analyzing consumer heterogeneity in the context of
eating-out behavior in Taiwan. The data set for this study
has been collected through a survey of 800 Taiwanese
consumers. The results of our data analysis show that the
neural network rule extraction algorithm is able to find
distinct consumer segments and predict the consumers
within each segment with good accuracy. [14]
A hybrid fuzzy-Delphi-AHP approach to propose a more
comprehensive framework with specific business elements,
and also points out six performance indices for firms to
adjust business strategy. In order to reduce business risk in
developing international markets, using the alliance model
is a key strategy for information service firms. On the other
hand, firms should handle more accurate business
information to support their business intelligence (BI)
system to make better business decisions. [30]
E. Integrated between BI and OLAP
3,33 % papers discuss about integrated between BI and
OLAP.
The use of business intelligence and OLAP tools in e-
learning environments and presents a case study of how to
apply these technologies in the database of an e-learning
system. The study shows that students spend little time with
course courseware and prefer to use collaborative activities,
such as virtual classroom and forums instead of just viewing
the learning material.[9]
The importance of Intelligence Systems as well as the
architecture of OLAP decisional interactive support systems.
[39]
F. Integrated between BI, Knowledge Management
1,67 % papers discuss about integrated between BI and
Knowledge Management.
The relation between business intelligence and
knowledge management is analyzed. The conceptual maps,
their objectives, components, construction failures, as well
as their main advantages for a significant learning are
defined as intelligence products. Finally, the execution of an
intelligence product is illustrated by using mapping and geo-
reference techniques aimed at facilitating a substantial
learning on the part of its users. [38]
G.Integrated between BI, Business Process Management
3,33 % papers discuss about integrated between BI and
Business Process Management
BPM implementation often combines financial with non-
financial metrics that can identify the health of an enterprise
from a variety of perspectives. BI and BPM applications
implement multidimensional models, powerful models for
data analysis and simulation. The present paper describes a
multidimensional model that supports the construction of the
master budget of an enterprise with simulation facilities.[43]
It is leverage the large data infrastructure investments
(e.g. ERP systems) made by firms, and have the potential to
realize the substantial value locked up in a firm's data
resources. Business investment in BI systems is continuing
to accelerate, there is a complete absence of a specific and
rigorous method to measure the realized business value, if
any. It is developed a new measure that is based on an
understanding of the characteristics of BI systems in a
process-oriented framework. [30]
H. Integrated between BI and Strategic Management
3,33 % papers discuss about integrated between BI and
OLAP.
How management of sustainability in organisations can
be supported by business intelligence (BI) systems. One
phase of any BI project, the information planning phase, i.e.,
the systematic way of defining relevant information in order
to integrate it in reporting activities. Using grounded theory,
the main contribution of this study is to propose a
conceptual model that seeks to support the process of
integration of socio-environmental indicators into
organizational strategy for sustainability [27]
Evidence suggests that some factors can determine the
successful implementation of strategic IT systems, i.e.
Business Intelligence operations they are painstakingly
difficult to implement. This paper identify some strategic
and tactical actions that Chinese CEOs can use to foster a
knowledge sharing culture that is conducive to BI systems
implementation.[44]
Business intelligence (BI) is a strategic approach for
systematically targeting, tracking, communicating and
transforming relevant weak signs into actionable
information on which strategic decision-making is based.
Despite the increasing importance of BI, there is little
underlying theoretical work, which directly can guide the
interpretation of ambiguous weak signs. It gives an insight
into the issue through a new strategic business intelligence
system called PUZZLE. It describe this system and validate
it by designing a prototype, test the system using in-depth
interviews, and hold learning sessions in order to further
knowledge about BI. [20]
I. Integrated between BI and ANP (Analytic Network
Process)
1,67 % papers discuss about integrated between BI and
ANP.
The electronization has enabled (BI) systems for the
purpose of decision-making. It is important to clarify the
impact factors of a BI system and find out a suitable
assessment method to evaluate the performance of BI
systems. An analytic network process (ANP) based
assessment model was constructed to assess the
effectiveness of BI systems. The results indicate that the
most critical factors that impact the effectiveness of a BI
system are: output information accuracy, conformity to the
requirements, and support of organizational efficiency. [53]
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I. Integrated between BI, Quality Management System
1,67 % papers discuss about integrated between BI,
Quality Management System.
Researches the application of Quality Management
Systems in ISO-9001:2000-standard-based Business
Intelligence Services. Some of the topics here in addressed
are as follows: concepts of Business Intelligence, its
services and products; ISO 9001:2000 Quality Management
Systems (QMS), their characteristics,
benefits/disadvantages; and the results of implementing an
ISO-9001:2000-standard-based QMS in a Center for
Business Intelligence Services. Also, there is an analysis of
the advantages and disadvantages generated by it for the
organization. [5]
J. Integrated between BI, CRM and Data Mining
As more retailers evolve into customer-centric and
segment-based business, business intelligence (BI) and
customer relationship management (CRM) systems are
playing a key role in achieving and maintaining competitive
advantage. When the first Fingerhut company peaked in
1998, as many as 200 analysts and 40 statisticians mined the
database for insights that helped predict consumer shopping
patterns and credit behaviour. Data mining and BI helped
Fingerhut spot shopping patterns, bring product offerings to
the right customers, and nurture customer relationships. [36]
K. Integrated between BI, DSS (Decision Support System),
Performance Scorecard
1,67 % papers discuss about integrated between BI and
Decision Support System, Performance Scorecard.
Office of Higher Education Commission uses Microsoft
SQL Server 2005 Business Intelligence Enterprise Data
Integration Tool to develop OHEC DSS and develop a web
application to develop the Executive Decision Support
System (DSS). It is developed a Performance Scorecard,
interactive and Business Insight Report after making BI [21]
L.Integrated between BI, AI, and Data Mining
5 % papers discuss about integrated between BI
(Business Intelligence), AI (Artificial Intelligence) and Data
Mining
The evolution of BI is divided into 3 stages: The
existence of a business information system that covers the
operational activities of the business and operational data,
historical data has been separated from operational data into
data warehouse designed to store and access data quickly,
BI systems currently involve data mining techniques and
artificial intelligence in the extract knowledge for decision
making.[20]
This papers discuss about efficient data mining tools and
presents an intelligent BI system framework based on many
computational intelligence paradigms, including a predictor
tool based on neuro-computing (cerebellar model articulation
controller neural network, CMAC NN), a classifier tool
based on neuro-computing (CMAC NN) and optimizer tools
based on evolutionary computing and artificial life (such as
real-coded genetic algorithm and artificial immune system).
[17]
Information about the benefits using Commercial Off-
the-Shelf (COTS) business intelligence software tools to
support aircraft and automated test system maintenance
environments. By using these engineering cluster models
produced earlier to develop and build more accurate
predictive models, predictive algorithms are utilized to
make use of the cluster results to improve predictive
accuracy. Common industry business intelligence Decision
Trees and Neural Network models are developed to
determine which algorithm produces the most
accurate models (as measured by comparing predictions
with actual values over the testing set). After an initial
mining structure and mining model is built (specifying the
input and predictable attributes),the analyst can easily add
other mining models. [12]
M. Integrated between BI,Data Mining, Knowledge
Management
1,67 % papers discuss about integrated between BI, Data
Mining and Knowledge Management
A novel model employing knowledge management in
data mining process to reduce data, analysis and action
latency of real-time business intelligence. [15]
N. Integrated between BI, Business Process Management
(BPM), Knowledge Management (KM)
1,67% papers discuss about integrated between BI, BPM
and Knowledge Management
That further opportunities for business value creation
could be discovered through systematic analysis of the non-
technical aspects of BI and BPM integration, especially in
terms of strategy alignment, human-centered knowledge
management and ongoing improvement of BI supported
processes. The paper proposes a theoretical framework
founded in the related research in BPM, BI and Knowledge
Management (KM) fields and describes how it has been
used to guide our empirical case study research in service
organizations in the context of BI-supported customer-
facing processes. [25]
O. Integrated between BI,CRM, SCM dan ERP
1,67 % papers discuss about integrated between BI,
CRM, SCM and ERP
Methods of raising corporation's decision-making ability
which based on Web service are introduced in this paper.
Several research results are also introduced here: the
application of some business software, such as Enterprise
Resource Planning, Customer Resources Management,
Supply Chain Management and so on; the corporation's
effective analysis of data; methods of building Business
Intelligence network. Cooperation of business intelligence
system and share of knowledge will be realized between
corporations. [52]
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P. Integrated between BI, ETL and OLAP
3,33 % papers discuss about integrated between BI,
ETL, OLAP
Business intelligence (BI) tools to take the mechanics
out of the process. Gartner's leading BI analysts highlighted
several major flaws: 1. Too many IT departments build a
data warehouse on the assumption that once it is built, users
will automatically see the benefit. 2. Reliance on
spreadsheets. 3. Data quality. The BI world is full of
technical terms, such as extract, transform & load (ETL) and
data warehouses. This may explain why the technology has
not done well in organizations with no IT department. First,
data must be extracted, usually from multiple sources, and
transformed (cleaned up) for consistency and accuracy.
Then it is loaded into a data warehouse that stores the data
in a logical way. ETL can account for 50% of the total cost
of a BI implementation. With an OLAP cube, you can
interactively slice and dice the data across multiple
dimensions and drill down for more detail.[26]
A process oriented to the addition of business
intelligence (BI) elements at Universidad de Tarapacá
(UTA), Arica, Chile. For the purpose, a data mart (DM) was
implemented, focused on the Admission and Registration
area of Academic Vice-Rectory. Its development required
carrying out activities such as to obtain business
requirements, to investigate the area key performance
indicator (KPI), to analyze several internal information
sources and to develop a dimensional model based on the
Kimball star schema. For proper implementation and
integration of these data repositories, extraction,
transformation and loading (ETL) processes were carried
out from two data sources. The creation of this DM, allowed
users of the Academic Vice-Rectory to visualize the
information they required through online analytical
processing (OLAP) tools. [10]
Q. Integrated between BI, Data Mining, Decision Support
System, Strategic Management
1,67 % papers discuss about integrated between BI, Data
Mining, Decision Support System, Strategic Management.
A Business Intelligence process for ISP dealers in Taiwan
to assist management in developing effective service
management strategies. It is explored the customers’ usage
characteristics and preference knowledge through applying
the attribute-oriented induction (AOI) method on IP traffic
data of users. Using the self-organizing map (SOM) method,
it is divided customers into clusters with different usage
behavior patterns. It is apply RFM modeling to calibrate
customers’ value of each cluster, which will enable the
management to develop direct and effective marketing
strategies. With actual data from one major ISP, it is
develop a BI decision support system with visual
presentation, which is well received by its management
staff.[23]
R. Integrated between BI, CRM and AI
1,67 % papers discuss about integrated between BI,
CRM (Customer Relationship Management) and AI
(Artificial Intelligence).
Many CRM researches have been performed to calculate
customer profitability and develop a comprehensive model
of it. This paper aims at providing an easy, efficient and
more practical alternative approach based on the customer
satisfaction survey for the profitable customers
segmentation. A multi-agent-based system, called the
survey-based profitable customers segmentation system that
executes the customer satisfaction survey and conducts the
mining of customer satisfaction survey, socio-demographic
and accounting database through the integrated uses of
business intelligence tools such as DEA (Data Envelopment
Analysis), Self-Organizing Map (SOM) neural network and
C4.5 for the profitable customers segmentation. A case
study on a Motor company's profitable customer
segmentation is illustrated.[17].
IV. OBSERVATION AND RECOMMENDATION
A. The most popular approach
The most popular approach is single approach Business
Intelligence System with 46,67 % of paper discuss it.
They discuss about the theoretic, method, model,
architecture, tools, system and case study of implementation
of Business Intelligence.
There are Business Intelligence from Voice of
Customer. There are Even Driven Architecture, process
oriented and service oriented architecture.
There are many software that is used in Business
Intelligence System research like SharePoint Server 2007,
Microsoft SQL Server 2005, Microsoft business intelligence
stack and BI products, and finally, describes how to deliver
BI solution with BI stack, and open source Business
Intelligence.
B. The most popular BI integrated research
The most popular BI integrated research is Integrated
between Business Intelligence and CRM System with 6,67
% papers. Integrated between BI, Data Mining 5 % and
Integrated between BI, AI and, Data Mining 5 %
The topic that integrated with BI research that is found in
this research is Supply Chain Management, Customer
Relationship Management, Data Mining, Data Warehouse,
Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management,
Artificial Intelligence, Enterprise Resource Planning,
Extract Transformation Loading, OLAP, Quality
Management System.
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I J E N S
Table 1. Topic that integrated with
Business Intelligence System
NO TOPIC SUM PERCENTAGE
1 Business Intelligence System 28 46.67%
2
Integrated between
BI,Supply Chain 2 3.33%
3
Integrated between BI, CRM
System 4 6.67%
4
Integrated between BI, Data
Mining 3 5.00%
5
Integrated between BI, AI
(Artificial Intelligence) 2 3.33%
6
Integrated between BI and
OLAP 2 3.33%
7
Integrated between BI,
Knowledge Management 1 1.67%
8
Integrated between BI and
BPM 2 3.33%
9
Integrated between BI and
Strategic Management 2 3.33%
10
Integrated between BI and
ANP 1 1.67%
11
Integrated between BI,
Quality Management System 1 1.67%
12
Integrated between BI,CRM,
Data Mining 1 1.67%
13
Integrated between BI, DSS,
Performance Scorecard 1 1.67%
14
Integrated between BI,AI
and,Data Mining 3 5.00%
15
Integrated BI,Data Mining,
Knowledge Management 1 1.67%
16
Integrated between BI,
Business Process
Management,KM 1 1.67%
17
Integrated between BI,CRM,
SCM dan ERP 1 1.67%
18
Integrated between BI, ETL
and OLAP 2 3.33%
19
Integrated between BI, Data
Mining,DSS,Strategy
Management 1 1.67%
20
Integrated between BI, CRM
and AI 1 1.67%
60 100.00%
C. Limitation of approaches
Limitation of approaches is sum of papers and the topic
that related to BI research. The topic that integrated with BI
research is in this research is Supply Chain Management,
Customer Relationship Management, Data Mining, Data
Warehouse, Decision Support System, Performance
Scorecard, Knowledge Management, Business Process
Management, Artificial Intelligence, Enterprise Resource
Planning, Extract Transformation Language, OLAP, Quality
Management System and Strategic Management
V. FUTURE WORK
The study was conducted to Milk Agro industry
scale Medium Enterprises in Indonesia. The systems
approach combined with the design of BI systems which
consists of 4 stages and 12 steps to obtain the BI system
prototype. The 4 stage is Analyze, Design, Planning,
Implementation and Controlling. The 12 steps is Need
Analysis; Problem Formulation; Business Need
Identification; Infrastructure Introduction; Planning the BI
Project; Identification Data, Data Warehouse, Data Mart
Definition; The definition of mathematical model needed;
Data Warehouse and Data Mart Development; Meta Data
Repository Development; ETL Development; Application
Development; Validation and Verification.
The research will integrated between BI, Data
Mining, Data Warehouse, OLAP, Artificial Intelligence,
Business Process Management and BI Scorecard.
Implementation and Controlling
Analize
Planning
Design
Agroindustry
Consumer
Supplier
Start
Need Analysis
Problem Formulation
Competitor
Government
Business
Environment
Business Need Identification
Infrastructure Introduction
Planning the BI Project
The definition of
mathematical
models needed
Project Need Breakdown
Identification Data, data
warehouse and data mart
definition
Prototype Development
Data Warehouse and Data Mart
Development
Meta Data Repository
Development
Application Development ETL Development
Validation and Verification
Suitable
Finish
Yes
No
BI System Model for Milk
Agro Industry
ETL: Extract, Transformation and Loading
Figure 1 Framework for Research Design Business
Intelligence System
VI. CONCLUSION
This paper reviews is based on a literature review on
business intelligence approaches the 60 journals of business
intelligence system. Relates articles appearing in the
international journal like Proquest, Ebscohost, Emerald,
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 103
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
Science Direct and IEEE Conference from 2000 to 2011 are
gathered
It was found 46,67 % research is in single approach
Business Intelligence System. Integrated between Business
Intelligence and Customer Relationship Management is the
most popular evaluating criteria with 6,67 %. Integrated
between BI, Data Mining 5 % and Integrated between BI,
AI and, Data Mining 5 %
The topic that integrated with BI research that is found in
this research is Supply Chain Management, Customer
Relationship Management, Data Mining, Data Warehouse,
Decision Support System, Performance Scorecard,
Knowledge Management, Business Process Management,
Artificial Intelligence, Enterprise Resource Planning,
Extract Transformation Loading, OLAP, Quality
Management System.
ACKNOWLEDGMENT
The first author thank to the Department of
Industrial Engineering, Trisakti University Jakarta Indonesia
for funding this research.
REFERENCES
1. Aciar, S. 2009. Adaptive business intelligence for an
open negotiation environment.Digital Ecosystems and
Technologies, 2009. DEST '09. 3rd IEEE International
Conference on Digital Object Identifier. Page(s): 517 –
522 IEEE Conferences
2. Al-Natsheh, H.T. 2010. Commercializing
computational intelligence techniques in a business
intelligence application; Congress Evolutionary
Computation (CEC), IEEE Congress on Digital Object
Identifier. Page(s): 1 – 7 IEEE Conferences
3. Barrento, M.P.A et al. 2010. Business intelligence
applied to Homogeneous Diagnostic Groups.
Information Systems and Technologies (CISTI), 5th
Iberian Conference, Page(s): 1 – 5 IEEE Conferences.
4. Barrett, Susan E. 2010. Competitive Intelligence:
Significance In Higher Education. Vol. 2 Issue 4. P26-
30. 5p.
5. Cartaya, Juan Carlos Carro. 2008. La inteligencia
empresarial y el Sistema de Gestión de Calidad ISO
9001:2000. Vol. 39 Issue 1. p31-44. 14p.
6. Cheng Yuan et al. 2010. The Research & Application
Of Process-Oriented Business Intelligence In
Manufacturing Industry Management And Service
Science (MASS), International Conference On Digital
Object Identifier. Page(S): 1 – 4 IEEE Conferences.
7. Chan Gaik Yee et al. 2010. Applying Instant Business
Intelligence In Marketing Campaign Automation.
Computer Research And Development, 2010 Second
International Conference On Digital Object Identifier,
Page(S): 643 – 646 IEEE Conferences
8. Edelman,Willian J. 2011.The "Benefit" Of Spying:
Defining The Boundaries Of Economic Espionage
Under The Economic Espionage Act Of 1996. Stanford
Law Review. Vol. 63 Issue 2, P447-474, 28p
9. Falakmasir M.H.et al.2010. Business intelligence in e-
learning: (case study on the Iran university of science
and technology dataset. Software Engineering and Data
Mining (SEDM). 2nd International Conference.Page(s):
473 – 477. IEEE Conferences
10. Fuentes Tapia, Louis. 2010. Incorporation Of Business
Intelligence Elements In The Admission And
Registration Process Of A Chilean University.
INGENIARE - Revista Chilena de Ingeniería. Vol. 18
Issue 3, P383-394, 12p.
11. Hsinchun Chen.2010. Business and Market Intelligence
2.0, Part 2. Volume: 25 , Issue: 2 Digital Object
Identifier. Page(s): 74 – 82 IEEE Journals.
12. Head, S.C et al. 2010.Using commercial off-the-shelf
business intelligence software tools to support aircraft
and automated test system maintenance environments.
IEEE Digital Object Identifier. Page(s): 1 – 6 IEEE
Conferences.
13. Habul, A. 2010. Business intelligence and customer
relationship management.; Information Technology
Interfaces (ITI), 32nd International Conference on,
Page(s): 169 – 174 IEEE Conferences.
14. Hayashi, Yoichi. 2010. Understanding consumer
heterogeneity: A business intelligence application of
neural networks. Knowledge-Based Systems. Vol. 23
Issue 8, p856-863, 8p
15. Houxing You.2010. A Knowledge Management
Approach for Real-Time Business Intelligence;a
Intelligent Systems and Applications (ISA), 2nd
International Workshop on Digital Object
Identifier.Page(s): 1 – 4 IEEE Conferences.
16. Jang Hee Lee, Sang Chan Park.2005. Intelligent
profitable customers segmentation system based on
business intelligence tools. Expert Systems with
Applications. Volume 29. Issue 1. Pages 145-152.
17. Jie Huang. 2010. Research on Mechanism and
Applicable Framework of E-Business Intelligence. E-
Business and E-Government (ICEE). 2010 International
Conference on Digital Object Identifier. Page(s): 195 –
198 IEEE Conferences.
18. Jui-Yu Wu. 2010. Computational Intelligence-Based
Intelligent Business Intelligence System: Concept and
Framework. Computer and Network Technology
(ICCNT). Second International Conference on Digital
Object Identifier, Page(s): 334 - 338 IEEE Conferences
19. Jun He et al. 2010. Research on EDA based Right-Time
Business Intelligence System. Information Management
and Engineering (ICIME), The 2nd IEEE International
Conference on Digital Object Identifier. Page(s): 476 –
479. IEEE Conferences
20. Kamel Rouibah et al. 2002. PUZZLE: a concept and
prototype for linking business intelligence to business
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 104
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
strategy. The Journal of Strategic Information Systems.
Volume 11. Issue 2. 1 June 2002. Pages 133-152.http://www.sciencedirect.com/science/article/pii/S0963
868702000057
21. Ko, I.S and Abdullaev, S.R. 2007. A Study on the
Aspects of Successful Business intelligence System
Development in Y.Shi et al. (Eds.):ICCS 2007.Part 4.
LNCS 4490. Pp.729-732. Spriger-Verlag Berlin
Heidenberg
22. Kleesuwan S, et all. 2010. Business Intelligence in
Thailand’s Higher Educational Resources Management.
Procedia Social and Behavioral Sciences 2.84-87. 2
23. Li, Sheng-Tun. 2008. Business Intelligence. Expert
Systems with Applications. Volume 35. Pages 739-754http://www.sciencedirect.com/science
24. Liu Luhao. 2010. Supply Chain Integration through
Business Intelligence; Management and Service
Science (MASS), International Conference on Digital
Object Identifier. Publication Year: 2010 , Page(s): 1 –
4 IEEE Conferences
25. Marjanovic, O. 2010. Business Value Creation through
Business Processes Management and Operational
Business Intelligence Integration a System Sciences
(HICSS), 43rd Hawaii International Conference on
Digital Object Identifier. Page(s): 1 – 10 IEEE
Conferences.
26. Maira Petrini et al.2009. Managing sustainability with
the support of business intelligence: Integrating socio-
environmental indicators and organisational
context. The Journal of Strategic Information Systems,
Volume 18, Issue 4, December 2009, Pages 178-191
27. Michael Burns.2005. Business intelligence survey. CA
Magazine.Toronto. Vol.138. Iss.5. pg 18.
28. Milkovi?, Vili et al.2009. An Analysis Of Device And
Equipment Failures By Means Of Business Intelligence
Methods. Transactions of FAMENA. Vol. 33 Issue 4.
p53-62. 10p.
29. Mircea, Marinela et al. 2009. Using Business Rules In
Business Intelligence. Journal of Applied Qualitative
Methods. Vol. 4 Issue 3, p382-393, 12p
30. Ming-Kuen Chen, Shih-Ching Wang.
.
2010.
The use of
a hybrid fuzzy-Delphi-AHP approach to develop global
business intelligence for information service firms.http://www.sciencedirect.com/science
31. Mohamed Z. Elbashir.2008. Measuring the effects of
business intelligence systems: The relationship between
business process and organizational performance.
International Journal of Accounting Information
Systems. Volume 9, Issue 3, September 2008, Pages
135-153. Eighth International Research Symposium on
Accounting Information Systems (IRSAIS)
32. Morris, Langdon. 2009.Business Model Innovation The
Strategy of Business Breakthroughs. International
Journal of Innovation Science. Vol. 1 Issue 4. p191-
204.14p
33. Najmi, Manoochehr et al. 2010. The evaluation of
Business Intelligence maturity level in Iranian banking
industry. 17
Th
International Conference Industrial
Engineering and Engineering Management (ICIEEM)
on Digital Object Identifier..Page(s): 466 - 470. IEEE
Conferences.
34. Neil Sutton. 2004. Bullish on business intelligence.
Computing Canada. Willowdale. Vol. 30
Iss. 14. pg. 24. 1 pgshttp://proquest.umi.com/pqdweb?did=723745941&sid=
2&Fmt=3&clientId=60726&RQT=309&VName=PQD
35. Nick Halsey. 2006. Enterprise Open Source BI in
Mission-Critical Applications: Dos and Don’ts.
Business Intelligence Journal . Vol. 14, No. 4
36. Phan, Dien D et al. 2010. A model of customer
relationship management and business intelligence
systems for catalogue and online retailers. December
2010, Pages 559-566. Elsevier B.V.
37. Piedade, M.B et a.2010. Business intelligence in higher
education: Enhancing the teaching-learning process
with a SRM system. Information Systems and
Technologies (CISTI), 5th Iberian Conference. Page(s):
1 – 5 IEEE Conferences.
38. Pina, Ramon Antonio Rodríguez et al.2008. Conceptual
maps and geo-references in business intelligence
products and services. ACIMED. Vol. 17 Issue 4. p91-
104. 14p.
39. Pirnau, M et al. 2010. General information on business
Intelligence and OLAP systems architecture.; Computer
and Automation Engineering (ICCAE), The 2nd
International Conference on Volume: 2 Digital Object
Identifier. Page(s): 294 – 297 IEEE Conferences.
40. Pillai, Jyothi.2011. User centric approach to itemset
utility mining in Market Basket Analysis. International
Journal on Computer Science & Engineering. Vol. 3
Issue 1. p393-400. 8p
41. Rodriguez, C.et al 2010. Internet Computing. IEEE
Volume: 14. 4 Digital Object Identifier , Page(s): 32 –
40. IEEE Journals
42. Sajjad, B. 2010. An open source service oriented
Mobile Business Intelligence Tool (MBIT).
Information and Communica tion Technologies, 2009.
ICICT '09. International Conference on Digital Object
Identifier: a Publication Year: 2009 , Page(s): 235 –
240 IEEE Conferences.
43. Sandu, Daniela Ioana. 2009. Multidimensional Model
For The Master Budget. Journal of Applied
Quantitative Methods. Vol. 4. Issue 4. p408-421, 14p
44. Seah Melody,et al.2010. A case analysis of Savecom:
The role of indigenous leadership in implementing a
business intelligence system. Pages 368-373. Elsevier
Ltd.http://www.sciencedirect.com/science/journal/
02684012
45. Stefanovic, N. and Stefanovic, D. 2009. Supply Chain
Business Intelligence: Technologies, Issues and Trends.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol: 11 No: 03 105
118503-6464 IJBAS-IJENS © June 2011 IJENS
I J E N S
in M. Bramer(Ed.): Artificial Intelligence. LNAI 5640.
IFIP International Federation for Information
Processing.
46. Subramaniam, L.V et al. 2009. Business Intelligence
from Voice of Customer. International Conference Data
Engineering, ICDE '09. IEEE 25th International
Conference on Digital Object Identifier. Page(s): 1391
– 1402 IEEE Conferences.
47. Xu Xi et al. 2010. Developing a Framework for
Business Intelligence Systems Integration Based on
Ontology.. ICNDS '09. International Conference
Networking and Digital Society on Volume: 2 Digital
Object Identifier. Page(s): 288 – 291 IEEE Conferences
48. Yeoh, William and Koronios, Andy.2010. Critical
Success Factor for Business Intelligence System.
Journal for Information System. Spring.
49. Yong Feng. et al. 2010 Intelligence System Based on
Multi-agent Design of the Low-Cost Business
Information Science and Management Engineering
(ISME), International Conference of Volume: 1 Digital
Object Identifier.
50. Yu, Zhaohui et al. 2010. The comparative study of the
Business Intelligence Explorer and the traditional.
Biomedical Engineering and Informatics (BMEI), 2010
3rd International Conference on Volume: 7 Digital
Object Identifier: a Publication Year: 2010 , Page(s):
2985 – 2989 IEEE Conferences
51. Yuantao Jiang. 2009. A conceptual framework and
hypotheses for the adoption of e-business intelligence.
Computing, Communication, Control, and
Management, 2009. CCCM 2009. ISECS International
Colloquium on Volume: 4 Digital Object Identifier.
Publication Year: 2009 , Page(s): 558 – 561 IEEE
Conferences.
52. Yujun Bao et al. 2009. Research of Business
Intelligence Which Based Upon Web; E-Business and
Information System Security. EBISS '09. International
Conference on Digital Object Identifier. Page(s): 1 – 4
IEEE Conferences.
53. Yu-Hsin Lin et al. 2009. Research on using ANP to
establish a performance assessment model for business
intelligence systems. March 2009, Pages 4135-4146http://www.sciencedirect.com/science
54. Yang Hang et al. 2009. A Framework of Business
Intelligence-Driven Data Mining for E-business; NCM
'09. Fifth International Joint Conference on Digital
Object Identifier. Page(s): 1964 – 1970 IEEE
Conferences.
55. Zhang Hai. 2009. Research Automated Negotiation
Framework for Business Intelligence Systems.
International Conference Networking and Digital
Society.. ICNDS '09. Digital Object Identifier. .
Page(s): 292 – 295 IEEE Conferences.
56. Zhang, Liyi. 2009. A Feasible Enterprise Business
Intelligence Design Model.a Management of e-
Commerce and e-Government, 2009. ICMECG '09.
International Conference on Digital Object Identifier:
Page(s): 182 - 187IEEE Conferences
57. Zhijun Ren. 2010. Constructing a Business Intelligence
Solution with Microsoft SQL Server 2005. Biomedical
Engineering and Computer Science (ICBECS),
International Conference on Digital Object Identifier,
Page(s): 1 – 4 IEEE Conferences.
58. Zhijun Ren. 2010. Constructing Business Intelligence
Solution with Share. Point Server 2007. International
Conference on Digital Object Identifier. Page(s): 615 –
618 IEEE Conferences.
59. Zhijun Ren et al. 2010. Delivering a Comprehensive BI
Solution with Microsoft Business Intelligence, a
Challenges in Environmental Science and Computer
Engineering (CESCE), International Conference
Volume: 2 Digital Object Identifier, Page(s): 278 – 281
IEEE Conferences.
60. Zuluaga, Givanni Gomez. 2011. Smart Decision
Infrastructure: Architecture Discussion.Cybernetics &
Systems. Vol. 42 Issue 2. p139-155, 17p
doc_280201885.pdf