Data Warehouse Model For Mobile-Based Applications

M S Abu Bakar Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 6, Issue 6, ( Part -1) June 2016, pp.34-41
www.ijera.com 34 | P a g e
Data Warehouse Model For Mobile-Based Applications
Muhammad Shahbani Abu Bakar1
, Azman Ta’a2
1
(School of Computing, College of Arts and Sciences, UUM, 06010 Sintok, Kedah, Malaysia)
2
(School of Computing, College of Arts and Sciences, UUM, 06010 Sintok, Kedah, Malaysia)
ABSTRACT
Analysis and design are very important roles in the Data Warehouse (DW) system development and forms as a
backbone of any successful or failure of the DW project. The emerging trends of analytic-based application
required the DW system to be implemented in the mobile environment. However, current analysis and design
approaches are based on existing DW environments that focusing on the deployment of the DW system in
traditional web-based applications. This will create the limitations on user accessed and the used of analytical
information by the decision makers. Consequently, this will prolong the adoption of analytic-based applications
to the users and organizations. This research aims to suggest an approach for modeling the DW and design the
DW system on the mobile environments. A variant dimension of modeling techniques was used to enhance the
DW schemas in order to accommodate the requirements of mobile characteristics in the DW design. A proposed
mobile DW system was evaluated by expert review, and support the success of mobile DW-based application
implementation.
Keywords: Requirement Analysis, Data Warehouse, Mobile Application, Information Science
I. INTRODUCTION
Nowadays, mobile phones and smart
phones have become very popular for a large
number of users (Ketmaneechairat, 2014). Current
information and communication technologies (ICT)
allow users to get important information instantly.
Smart phones are mainstream in this area with
active iOS and Android devices surpassing 700
million globally (Smirnov, Kashevnik, Shilov,
Teslya, & Shabaev, 2014). Moreover, mobile
technology is rapidly utilized in various computer
systems, including Data Warehouse (DW), Business
Intelligence (BI) system, and Data Analytic (DA).
As reported by Borg and White (2010), the BI usage
among organizations with mobile BI has increased
double as compared to immobilized users as shown
in Figure 1.
Fig. 1: Mobility in BI Usage
At the beginning, DW is designed for
operational-based information to support strategic
decisions making in the organization. However, the
information requirements in the organizations today
need to support customer satisfaction, enhance
administration, improve financial management,
increase business activities, and assist human
resources tasks. Making decisions in these areas
must be made quickly based on the historical and
current data through the implementation of the latest
technologies that enable to store, retrieve,
manipulate, analyze, produce reports and
communicate with the captured data of the
organization. The decision makers such as Chief
Executive Officer (CEO), General Manager (GM),
and Directors of agencies are seeking to synthesize
and analyze data as well as information in a real-
time manner for implementing better strategies.
This scenario is based on the current
economic situation that needs faster and effective
management of the available resources in order to
achieve the organizations, clients, and community’s
requirements. The requirements of meaningful and
analytical information in real-time is essential for
organizations provide better services, which enable
better decision-making. However, in order to
provide the real-time DW-based application, the
organization needs to develop centralize mobile DW
system that systematically collects and analyzes the
heterogeneous data sources in seamlessly accessed
and processed. Therefore, the challenge to build DW
system is to model and design the DW system
according to the mobile technology environment.
RESEARCH ARTICLE OPEN ACCESS

Nowadays, several methods for designing
mobile applications have been used by developers.
However, most of the methods focus on the
operational-based applications. Thus, a new proper
and systematic method is required in modeling and
designing the mobile DW system according to the
users and organization requirements. The method
should be able to accommodate with the mobility
characteristics, which is concerned about the
availability of DW at any place, any time, and any
device. Moreover, the method must be able to
extend to other kinds of mobile applications.
I. THE PROBLEM
DW systems have been used mainly to
support decision making. These days, the decision-
support systems (DSS) were developed based on
DW to provide strategic information for analysis,
behold trends, and monitoring performance
(Ponniah, 2010; Shahbani & Shiratuddin, 2011).
However, traditional DW is passive, historical-based
data, batch-processing, and not real-time. The
current trend for DSS required new DW model to
support analytical decision making. It needs to
extract from heterogeneous data sources for
providing online and real-time data analytics.
Indeed, the attention given to the DW systems is
focused on the mobile-based application that
supports the new system environment.
In the new report by Forrester, a mobile
DW-based application will be the norm in most
organizations (Evelson & Yakkundi, 2012).
Knowledge workers will make decisions at any
places and anytime. Furthermore, vendors will
respond with more features for multiple visual query
methods such as GPS analytics and integration with
enterprise mobile ERP applications. Meantime,
various platforms, portal, social functionality and
other tools will be increased and integrated into a
user friendly system called information workplaces.
As anticipated by Information Systems Audit and
Control Association (ISACA), the uses of mobile
devices are to access and deal with data is increased
for the coming year. Moreover, this was a top trend
in the Information Technology (IT) field, which is
the consumerization of IT and the proliferation of
mobile BI (Kern, 2012). One of the major problems
that need to consider is how to spread and share the
analytical information from the related agencies to
the farmers and support the farmers with the right
and accurate information for decision making. This
paper aims to model and design the mobile DW
system to support the implementation of portable
Paddy Management System (PMS) for benefiting
the farmers.
II. MOBILE DATA WAREHOUSE
The question is whether the DW systems
can be modeled and designed from the perspectives
of mobile environments need to be explored, and
presented the design process for mobile DW
systematically. Importantly, the design method will
be based on the existing DW modeling (i.e.
Dimension Modeling) in order to maintain the
consistency of the model throughout the design
process. Moreover, the Dimension Modeling (DM)
was proven to be successful used in building DW
systems since 1996 (Kimball, 1996; Rizzi, 2007),
and most of the new methods are based on the DM
paradigm. The concepts of fact, dimension, and
measure in DM paradigm will be organized with
mobile characteristics in introducing the method for
DW design.
Generally, any software systems that
support mobility is orchestrated into two distinct
types of hosts: mobile and fixed hosts (i.e., base
stations) (Pitoura and Bhargava, 1994). Mobile
hosts have limited computing power, memory, hard
disk, and display screen due to their small size and
weight (e.g., iPhone, Palm Top, Tablet). The
characteristics of the mobile hosts that normally
operate in wireless connectivity are identified as low
bandwidth, always disconnected, still expensive,
small size, small display, limited battery power,
easy to lose, and so on. These mobile characteristics
should be taken into DW design and
implementation.
For example, to access and analyze the
heterogeneous data sources, disconnected scenario
will be aroused. This will cause an error-prone in
the results and produced inconsistent information to
the users. Instead of resolving on a physical part
(e.g., increase wireless signal or bandwidth size), the
design of the DW structure should be able to capture
the intermediate results, and finally consolidated or
migrated at the available connection. This will be
explored and proposed the workable design method
to overcome these problems.
Mobile DW made sense in the most
specific scenarios such as sales representatives want
to promote their business to the prospect client. In
the competitive business rival, the sales
representatives need to meet client face-to-face and
presented the real information at the real-time. With
the minimal processing capabilities of the mobile
devices (e.g., mobile phone) and prevent to perform
deep analytics, a simple set of extracting data from
mobile DW is enough to provide the required
information. Importantly, the consistency and
reliability of the information are controlled by DW
structured. A mobile DW is designed to meet
business needs that consistent and reliable as
accessed by the desktop PC or fixed hosts.
Moreover, by supporting the appropriate mobile

tools or platforms, the development of the mobile
DW applications is going to be demanding for the
DW developer.
This research is to define user requirements
for mobile DW systems and propose a method to
design the system in mobile environments. The
designing of a system will be translated into a
prototype mobile DW system, which is developed
within the telecommunication domain. To ensure
best performances of the mobile DW systems,
feedbacks from the users will be evaluated. The
adjustment will be made for a mobile DW design
that fits into the requirements of the users.
The proposed architecture for mobile DW
implementation is shown in Figure 2, which is
comprised of the relationships between eight main
activities in mobile DW system development.
Fig. 2: Mobile DW Architecture
All the relationships represent the process
within the development, which is started from
identifying requirements, building DW schemas and
ETL specifications, process, transform and loading
the data, and produce the data for user’s utilization.
The ETL specifications as series of a process of data
transformation (Ta’a, Abdullah, & Norwawi, 2011)
will be defined according to the mobile application
requirements. The alignment of the development
activities with the general research methodology is
presented in Table 1, which shows how the steps
correspond to the research methodology.
Table 1. Mobile DW System Development
N
o
Activities Phases Process
1. Gather and
elicit mobile
DW
requirement
s
Requiremen
t gathering
and
elicitation.
Data
gathering and
elicitation
based on
perspectives
2. Analyze
mobile DW
requirement
s Requiremen
t Analysis
User and
organizationa
l based
analysis of
facts,
dimension
and attributes.
3. Analyze
mobile DW
integration
requirement
s
4. Design
mobile DW
schemas
with mobile
characteristi
cs
Modeling
and
Designing
Modeling and
designing the
DW structure
for mobile
5. Design
mobile DW
specification
for data
Integration
Modeling and
Designing the
ETL
processes
6. Develop a
Mobile DW
Application
Implemen-
tation
Prototype
Development
7. Validate and
Evaluate the
Mobile DW
Application
Validation
and
Evaluation
Validation
and
Evaluation
III. CASE STUDY: PADDY MANAGEMENT
IN YAN, KEDAH
Currently, the only effective mechanism for
informing farmers about agriculture activities is
through television. However, only few farmers,
managers and agents were receiving the updated
information about the paddy farming activity
schedule, paddy crop information, manual
procedures and guidelines, government policies and
more. The limited distribution of information to the
farmers has been studied by researchers and finding
that the information sharing using the television is
effective, but with the limited air time frame for
agriculture segments, the user can’t have enough
information about the agriculture activities (Hassan,
Mohamed Shaffril, Abu Samah, Shahkat Ali, and
Ramli, 2010).
Mobile technology is the technology that
can be used anywhere, any devices and anytime.
Therefore, the development of the Paddy
Management System (PMS) must be suited to the
concept of the portable PMS, which can be accessed
from anywhere and anytime. The PMS is delivering
the information to the users in order to guide
farmers to increase the productivity and income by
using the right information for planning, farming,
and harvesting the paddy plantation. The case study
for PMS will be based on the paddy management in
Kedah state.
Requirement elicitation and analysis
The information about paddy management
is handled by the related agencies such as Muda
Agriculture Development Authority (MADA), Padi
Beras Nasional Berhad (BERNAS), Department of
Irrigation and Drainage Malaysia (JPS), Malaysian
Agricultural Research and Development Institute
(MARDI) and several more was not integrated to be

one “database” that can be shared by the farmers,
managers and agents, especially in helping farmers
for farming their paddy plantation.
The Paddy Management System includes
the process of gathering, processing, storing and
distributing of data to carry out the operation
functions of the farm in the form of the information
needed (Sorensen et al., 2010). In order to collect
and process data, interviews have been conducted
with the farmers to identify the requirement of the
system. In addition, we also carry out a literature
reading to get more information about the
requirement of paddy management. The results from
the interview sessions and literature reading can be
formulated into the system requirement as Figure 3.
Fig. 3: Paddy Planting Practices
Mobile DW in PMS consist of data that
developed from several databases that supply the
information to the system as shown in Figure 3.
Each of the databases comes from a different
location and combined as the DW system for
integration and data analysis. The requirements
collected and analysts will be used to design the DW
according the mobile elements. Each data is analysis
to the specific user level and location. The data
provided to the DW system will display the
information for the PMS users, including the
important information or announcement from the
information provider. Indeed, the information can be
shared among farmers by using mobile devices and
can be accessed anywhere, any devices and anytime
with the availability of the internet connection.
Fig. 4: The User Domain for PMS
Fig. 5. The Use Case of the PMS
Based on the Figure 4, the various data
sources are collected from several related agencies
in different locations. Each of the agencies supplied
the data to the DW and performs the analysis and
integration to load the required data into the DW.
The information produced from the analysis is ready
to access by the farmers, managers and agents as
shown in use case model in Figure 5. The functions
showed in Figure 5 are used to design the mobile
DW structure for portable PMS. The main
information provided by the DW comprises of seed
analysis, fertilizer analysis, farming scheduling
analysis, and control analysis. The evaluation will
determine the correctness of the DW model through
expert review. The result of the DW model is used
to develop the prototype for PMS it will simulate the
functionality of PMS to deliver the information as
requested by the farmers or agencies.
Mobile Data Warehouse Design
The mobile DW is designed based on the
dimensional modeling that contains fact, dimension
and measure components in the table schema. Based
on the requirement analysis presented in Figure 5,
the components of DW model are listed in Table 2.

Table 2: Components of DW Model for PMS
DW
Components
Description
Fact Paddy Farming
Dimensions Season, Farmer, Seed, Crop,
Lot
Measures Size, Yield, Farming Cost,
Profit Cost, Seed Cost, Plough
Cost, Fertilizer Cost, Poison
Cost, Harvest Cost, Subsidies
Cost, Other Cost, Percent Profit
The measures defined the information
required by the users, which are comprised of total
cost of farming such as seeds, plough, fertilizer, and
poison. Other information is size area, yield, cost
profit, subsidies cost, harvest cost, and percent
profit. All these components are presented in DW
modeling as shown in Figure 6.
Fig. 6. Mobile DW Model for the PMS
Prototype for PMS
The PMS prototype consists of the six
functions of seed analysis, control analysis, harvest
analysis, fertilizer schedule, scheduling info, and
agency info stated in the use case. All of these
functions developed based on the data stored in the
DW for PMS. The prototype is developed using the
Microsoft Visual Studio 2010 based on the
Microsoft ASP.NET MVC framework and
Microsoft.NET 4.5 framework that connected to the
DW system that build on the Microsoft SQL Server
2008.
The interfaces are designed for mobile
characteristics and all of the functions can be
accessed by mobile devices and personal computer.
It is important to make sure the user of the mobile
devices can access, read and use the system
smoothly. The system also can be accessed from the
computer with the web browser interface. An
example of the interfaces designed for PMS is
shown in Figure 7 and Figure 8.
Fig. 7: Main Menu
Fig. 8: Seed Analysis Page.
Figure 7 shows the main menu of the PMS, and
Figure 8 shows one of the analysis functions
provided by the prototype system that is the seed
analysis system. This function is for determining the
suitable seed for the planting process of the paddy
field. The attributes such as field size, water level,
type of soil and decease are shown in the area. The
entire attribute insert to the system or by collecting
the information for the personal user information,
the system will determine the suitable seed for the
planting process.
IV. EVALUATION
The model evaluation was done by using expert
review technique. Two focus group sessions were
conducted. The purposed of the session is to get
feedback from the experts (nine experts) on the DW
mobile model and developed prototype. The
sessions involved two rounds; the first was open
discussion where they were asked eight of quality
model dimensions that are visibility, complexity,
compatibility, flexibility, clarity, effectiveness,
manageability, and evolutionary of the model. The
second round required the respondents to go through
the prototype of PMS. The respondents were asked
to complete an instrument measuring eight
dimensions indicates in the research. The result
shows the proposed model generally has moderate
strength of five dimensions and high strength in

three dimensions that are visibility, clarity, and
effectiveness. The strength of the model can be
illustrated in Figure 9.
Fig. 9: Evaluation of the Mobile DW Model
V. RESEARCH WORKS
Since 1999, several methods and approaches
have been suggested to develop and implement the
mobile DW due to the emergence of mobile
technology. Most of the suggestions are interested in
the method to implement the DW system in mobile
environments. However, we argued, a new method
is required in order to design and implement a
mobile DW-based application due to the current
DW system always depended on the success of the
DW design process (Inmon, 1992; Kimball, 1996;
Lujan-Mora, 2005; Rizzi, 2007). Existing methods
were not focused on the design process, and
neglected the important roles of mobile
functionalities. Generally, a review of various
methods and approaches used in the design and
implement mobile DW system is shown in the Table
3.
Table 3: Methods Used in Mobile DW System
Researche
r/Develop
er
Methods Used for
Mobile DW
Descriptio
n
Ken
(1999)
Mobile Warehousing
System (MoWS)
Propose a
framework
to
investigate
issues for
improving
the
performan
ce of query
processing
.
Stanoi et
al. (1999)
Data Warehousing
Alternatives for
Mobile Environments
Propose
technique
for
managing
DW in
mobile
environme
nt
Weippl et
al. (2000)
Mobile Database
Agents for Building
Data Warehouses
Propose a
pragmatic
approach
for
applying
mobile
agent
technology
within
distributed
database
manageme
nt systems
(DBMSs)
Choy et al.
(2000)
Distributed Database
Design for Mobile
Geographical
Applications
Investigate
GIS-based
system and
propose
system
architectur
e for ATIS
that based
on GIS
and
distributed
database
(e.g., DW)
technology
Chi et al.
(2002)
Agent Warehouse - A
New Paradigm for
Mobile Agent
Deployment
Describes
a novel
concept of
agent’s
warehouse
.
Huang et
al. (2008)
Intelligent Cache
Management for
Mobile Data
Warehouse Systems
Proposes
an
intelligent
cache
mechanis
m for a
DW
system in a
mobile
environme
nt
Oueslati &
Akaichi
(2010)
Mobile Information
Collectors' Trajectory
Data Warehouse
Analyze
complex
phenomen
a involve
moving
objects,
Trajectory
DW for
decision
problems
concerned
with
mobility

Gaspar et
al. (2011)
Design and
implementation of a
client warehouse
application over an
enterprise resource
planning system for
mobile devices
Proposes a
client
warehouse
application
over
Microsoft
Dynamics
Navision
5.0 ERP
Motskin et
al. (2011)
Network
Warehouses:
Efficient Information
Distribution to
Mobile Users
Propose a
strategy to
distribute
select a set
of well-
placed
nodes
(warehous
e)
VI. CONCLUSION
This research aims to model and design the DW
system for mobile environments. A variant
dimension modeling technique is suggested to
enhance the DW modeling for accommodating the
mobility characteristics of the DW design. Indeed,
knowledge in mobile computing is required to
understand the process involved in the mobile DW
design. This is important to ensure the DW design
will fulfill the requirements of mobile characteristics
and support the implementation of the DW system
in a mobile environment. The information about
Paddy Management System (PMS) is used for
testing the proposed mobile DW modeling. The
farmers, managers and agencies of MADA,
MARDI, JPS and other related agencies will be
provided the mobile technology that can use the
PMS anywhere, any devices and anytime. The PMS
shares the valuable information on the agriculture,
specifically for paddy plantation to be useful for the
users and make their farming activity easy, direct
and successful regardless location, devices and time.
By using the PMS, it is encouraging the farmers,
managers and agencies in paddy planting activity
make better decision, and keep informed about their
planting activities instantly.
ACKNOWLEDGEMENTS
This work was supported by the Grant
Leadership Development Scheme (LEADS). The
authors wish to thank the Ministry of Higher
Education of Malaysia (MOHE) and Research
Innovation and Management Center (RIMC) of
Universiti Utara Malaysia (UUM) for providing the
opportunities to perform this research.
REFERENCES
[1]. Borg, A., & White, D. (2010). Mobile BI:
Actionable Intelligence for the Agile
Enterprise: Aberdeen Group.
[2]. Choy, M. K., & Leong, M.-P. H. V. (2000).
Distributed Database Design for Mobile
Geographical Applications. JOURNAL OF
DATABASE MANAGEMENT, 11(1), 3-
15.
[3]. Evelson, B., & Yakkundi, A. (2012). The
Future Of BI - Top 10 Business
Intelligence Predictions For 2012
[Electronic Version]. Information
Management. Retrieved 20/1/2012.
[4]. Gaspar, V., Madarasz, L., Paralic, J., &
Tenaiova, K. (2011). Design and
implementation of a client warehouse
application over an enterprise resource
planning system for mobile devices. 3rd
IEEE International Symposium on
Logistics and Industrial Informatics
(LINDI)
[5]. Huang, S., Lin, B., & Deng, Q. (2008).
Intelligent Cache Management for Mobile
Data Warehouse Systems. In J. Wang
(Ed.), Data Warehousing and Mining:
Concepts, Methodologies, Tools, and
Applications (pp. 1539-1556).
doi:10.4018/978-1-59904-951-9.ch088
[6]. Inmon, W. H. (1992). Building the data
warehouse: Wellesley.
[7]. Ken, L. C.-k. (1999). Data warehousing
support for mobile environment. Hong
Kong Polytechnic University.
[8]. Kern, J. (2012). How to Manage Top
Trends of 2012 [Electronic Version].
Information Management. Retrieved
5/3/2012.
[9]. Ketmaneechairat, H. (2014). Searching
Application for Southern Thailand Travel
Guide on iPhone, 195–200.
[10]. Kimball, R. (1996). The Data Warehouse
Toolkit - Practical Techniques for Building
Dimensional Data Warehouses: John Wiley
& Son.
[11]. Lujan-Mora, S. (2005). Data Warehouse
Design With UML. Unpublished PhD,
University of Alicante.
[12]. Md. Salleh Hassan, Hayrol Azril Mohamed
Shaffril, Bahaman Abu Samah, Muhamad
Sham Shahkat Ali, & Nor Sabila Ramli.
(2010). Agriculture Communication in
Malaysia: The Current Situation. American
Journal of Agriculture and Biological
Sciences 5(3), 389-396.
[13]. Motskin, A., Downes, I., Kusy, B.,
Gnawali, O., & Guibas, L. (2011). Network

warehouses: Efficient information
distribution to mobile users. Proceedings
IEEE INFOCOM, 2011.
[14]. Oueslati, W., & Akaichi, J. (2010). Mobile
Information Collectors' Trajectory Data
Warehouse Design. International Journal of
Managing Information Technology,
abs/1009.0397, 20.
[15]. Ponniah, P. (2010). Data Warehousing -
Fundamental for IT Professionals: A John
Wiley & Son, Inc.
[16]. Rizzi, S. (2007). Conceptual Modeling
Solutions for the Data Warehouse. Idea
Group Inc., 1-26.
[17]. Shahbani, M., & Shiratuddin,N., (2011).
Conceptual Design Model Using
Operational Data Store (CoDMODS) for
Business Intelligence Applications.
International Journal of Computer Science
and Network Security (IJCSNS), 11(3).
[18]. Smirnov, A., Kashevnik, A., Shilov, N.,
Teslya, N., & Shabaev, A. (2014). Mobile
Application for Guiding Tourist Activities :
Tourist Assistant – TAIS.
[19]. Sørensen, C. G., Fountas, S., Nash, E.,
Pesonen, L., Bochtis, D., Pedersen, S. M.,
... & Blackmore, S. B. (2010). Conceptual
model of a future farm management
information system. Computers and
electronics in agriculture, 72(1), 37-47.
[20]. Stanoi, I., Agrawal, D., Abbadi, A. E.,
Phatak, S. H., & Badrinath, B. R. (1999).
Data warehousing alternatives for mobile
environments. Paper presented at the
Proceedings of the 1st ACM international
workshop on Data engineering for wireless
and mobile access.
[21]. Ta'a, A., Abdullah, M. S., & Norwawi, N.
M. (2011). Goal-Ontology ETL Processes
Specification. Journal of Information and
Communication Technology (JICT), 10,
15-43.
[22]. Weippl, E., Altmann, J., & Essmayr, W.
(2000). Mobile database agents for
building data warehouses. Paper presented
at the Database and Expert Systems
Applications, 11th International Workshop,
London, UK.

Data Warehouse Model For Mobile-Based Applications

More Related Content

What's hot (20)

Similar to Data Warehouse Model For Mobile-Based Applications (20)

Recently uploaded (20)

Data Warehouse Model For Mobile-Based Applications