Download Design of OLAP Cube for Banking System of India

International Journal of Computer Trends and Technology (IJCTT) – Volume 35 Number 3- May 2016
Design of OLAP Cube for Banking System of
India
Dr. Arpita Mathur
Nikhita Mathur
Assistant Professor, Department of Computer Science,
Lachoo Memorial College, Jodhpur, Rajasthan, India
Abstract — Nowadays OLAP is playing a very
important role in banking sector in India. In India
banks are providing various services to attract
their customers due to tough competition. There
are a lot of changes seen in recent years in the field
of banking industry. Now banks are adopting
innovative ideas for improving their services and to
get the faith of their customer .These innovative
services comprise of: centralized banking system,
mobile banking , internet banking , NACH, SMS
alert , RTGS, smart card, ATM and many more.
This paper presents OLAP & data mining
strengths which link up the decision support system
of banking sector. The objective of this paper is to
show which model is purposed for banking industry
& how that model work for improving the
efficiency.
Keywords —Data warehouse, OLAP, Data cubes,
Decision Support System, Data Mining.
I.
INTRODUCTION:Data warehouse is used to store large amount of
data which is used to get and share the information.
The main objective is to use information anytime
and anywhere. Nowadays our banking system is
used on internet. Through internet, banking
industry communicate as the way of transaction. As
technology goes higher many decision support
applications were developed. Multiple frameworks
are used by researchers for building up this system.
In this paper we focus on Indian Banking System
based on decision support application, model, data
cube, and query and reporting tools. The
technologies which are used in banking sector
based on decision support system.
Research Scholar, Department of Computer Science,
Pacific University, Udaipur, Rajasthan, India
manipulate the multitude of complex factors before
taking a decision. This paper covered three sectors:
OLAP, Application of data mining in banks and
Decision Support System. To build Decision
Support System industry or organisations generally
use OLAP rather than OLTP.
1.2. Data Warehouse:
It is a very large database system that collect,
summarizes and stores data from multiple remote
and heterogeneous information sources [1]. It is a
Database used for reporting and analysis.
According to the need of individual user, the
dimension of Database application designs
represents their attributes such as name of
customer, type of account etc. Data warehouse
using two techniques OLTP and OLAP. Table 1
show the difference between OLAP and OLTP to
overcome the drawback of OLTP.
OLTP
OLAP
Online Transaction Online Analytical
Processing
Processing
2-D view of data
Multi-dimensional
view
Source of data is Consolidate data
operational
(its data comes
data(there
are from the various
original source of OLTP database)
the data)
Its data purpose is To help with
to control and run planning problem
fundamental
solving
and
business task
decision support
It focus on updating Reporting data
data
It uses normalized It
uses
star,
schema
snowflakes
and
constellation
schema
1.1. Decision Support System:
Decision Support System is an application used for
management information system whose objective is
to help decision maker to analyze, evaluate, and
ISSN: 2231-2803
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The queries
simple
are
Complex queries
Page 154
International Journal of Computer Trends and Technology (IJCTT) – Volume 35 Number 3- May 2016
Fast
speed
of
processing
It requires small
space
Slow speed of
processing
Need large space
Table 1. Difference between OLAP and OLTP
Today data warehousing system support
sophisticated multi-dimensional analysis which is
data mining technology. According to Feng Lei,
Chen Hexi the main aim of data warehouse is to
build a systematic data storage environment and
separate plenty of data needed by analysis and
decision making from traditional operation
condition. That makes transfer dispersive and
disaccord operation data to integrate and uniform
information [2].
Fig.2: ER Diagram
SQL command that consisting six tables.
Select c. Name, c. Age from customer c. employee
e. branch b. time t. account a, trans tr where c.
cust_id = tr. cust_id and tr. emp_id = e,emp_id and
tr.time_id = t.Time_id and tr.acc_id group by
c.Name, c.Age
In OLAP large amount of data require many joins
for normalization form to answer a simple queries.
As we know in banking system there are many
tables so the time taken to process the joins will be
unacceptable. To overcome this big drawback
OLAP was introduced.
OLAP uses multidimensional tables called data
cubes or OLAP cube. Table 1 shows the difference
between OLAP and OLTP In OLAP technology
data analyze in several dimensions and also
incorporate query optimization [3].To search for
relevant information user can filter, slice and dice,
drill-down and roll-up the data.
II.
MODEL PROPOSED FOR BANKING
SYSTEM
2.1. Star Schema:
We know that OLTP system support data
modelling or for recording based business
transaction. All the information are view in 2-D. To
access this information typically we use SQL
(Structured Query Language) and then process
information comes with the result in the form of
“reports”. Fig. 2 shows E-R Diagram of Banking
System.
The process of examining or extracting large preexisting data stored in database in order to generate
new information is known as Data Mining.
Data Mining is now became an important research
tool. It also discovers hidden knowledge in the
data.
Location
ALL
Country
INDIA
State
RAJASTHAN
City
AJMER
JODHPUR
EUROPE
GUJRAT
JAIPUR
Fig 3. A concept hierarchy for different
locations
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International Journal of Computer Trends and Technology (IJCTT) – Volume 35 Number 3- May 2016
III.
DATA CUBE:
We know that OLAP is multi-dimensional data
modelling techniques and to view the data is in the
form of data cube. It is a combination of facts and
dimension. To implementing the system firstly data
cube is going to be created then the data mining
process is started. The OLAP cube stores the
information and allows browsing at different
conceptual levels. Data mining can be applied on
any dimensions of the cube. After the model is built
it is stored in the OLAP cube [12]. As shown in fig
4 each dimension represents the rule to their
corresponding node in the decision tree mining
model.
OLAP operations describe the different levels of
the system. The data of cooperative banks is been
used for comprise of saving account database, fixed
deposit database and recurring deposit database.
We explain this with the help of dummy attributes
.After this we will do implementation of data cube
is through queries. And then we will get the final
report.
[5]
Usama F., Data Mining and Knowledge Discovery in
Databases: Implications for Scientific Databases.
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[9] Han, J., Chiang, J.Y., Chee, S., Chen, J., Chen, Q., Cheng,
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[12] Dr. Harsh .D and Suman K.M., Design of Data Cubes and
Mining for Online Banking System, IJCA, vol 30- no.3,
2011,September
Fig 4: A Logical view of OLAP Cube
IV.
CONCLUSION
From the above work, it can be concluded that
OLAP technology, data mining technique and
OLAP Cube are the best way of fast searching data
from large amount of database with in a fractions
of seconds. Data cube store large banking data
which are used by the administrator or customer.
After implementing the data cube, through SQL
queries it will solve banking related problems and
automatically converts those problems to multi
dimensional base queries.
REFERENCES
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http://www.ijcttjournal.org
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