3.4. Database Applications

Introduction to Database Applications

With the fast moving world there is a shift from simply accessing the data contained in a database to managing & manipulating the content of a database to producing useful information. Among the database manipulating activities,
Searching, filtering, synthesizing, assimilating are important.

With the help of databases it is possible to,
• Link the company databases to the Internet
• Set up data warehouses and marts
• Use databases for strategic business intelligence
• Place data at different locations
• Use online processing and open connectivity standards for increased productivity
• Develop databases with the object oriented approach
Search for and use unstructured data such as graphics, audio & video

3.4.1 Linking the Company Database to the Internet

Today customers, suppliers and company employees must be able to access corporate database
through the internet, intranet and extranet to meet various business needs.

Example:
1. When a customer is going to buy a book through internet he is accessing a database to
find the book information, author, price, etc.

2. With the help of the databases the suppliers can check the raw materials and the
current production schedule to determine when & how much of their products must be
delivered to support just-in-time inventory management

3. Employees Of a company working from abroad may want to access the internal
databases through the Internet or the intranet to make important decisions.

Developing a seamless integration of traditional databases with the internet is often called a
semantic web. The semantic web is about taking the relational database & webbing it. It allows
accessing and manipulating a number of traditional databases at the same time through the
internet.

Instead of the internet, organizations are gaining access to databases through networks to get
good prices and reliable services. However, linking company databases to external network such
as the Internet can be potentially dangerous due to issues related to security. For example a
competitor or any other hacker may gain access to these databases.

3.4.2. Data warehouses, Data Marts and Data Mining

The raw data necessary to make sound business decisions is stored in a variety of locations and
formats.

Using data warehouses, data marts and data mining this data can be used to support decision
making.

Data Warehouses



A data warehouse stores data that have been extracted from the various operational,
external and other databases of an organisation. It is a central source of the data that have
been cleaned, transformed, catalogued so that they can be used by managers and other
business professionals for data mining, online analytical processing and other form of
business analysis, market research and decision support. A data warehouse can also be
viewed as a database for historical data from different functions within a company.
A data warehouse is designed specifically to support management decision making, not to
meet the needs of transaction processing systems. The structure of data warehouse is
easier for end users to navigate, understand and query against unlike the relational
databases primarily designed to handle lots of transactions. Data warehouse enable
queries that cast across different segments of a company’s operation.

Example: Production data could be compared against inventory data even if they were
originally stored in different tables with different structures. Data warehousing is an efficient
way to manage and report on data that is from a variety of sources, non uniform and
scattered throughout a company.

For the purpose of keeping the data fresh and accurate data warehouse need regular
updates. Updating of the data warehouse must be fast, efficient and automated in order to
retain the value of the data warehouse.

A primary advantage of data warehousing is the ability to relate data in new, innovative
ways. However, establishing a data warehouse can be difficult and costly.

Data Marts

This is a sub set of a data warehouse. Using this concept it is possible to store a subset of
the data for a single aspect of a company’s business rather than saving all enterprise data
in one monolithic database.

Example: Finance, inventory, personnel

A data mart could provide more details about a specific area than the data warehouse
would provide. Data marts are useful for smaller groups such as a particular department in
an organization to access a detailed data while a warehouse contains a summary data that
can be used by an entire company. Typically, as data marts contain less giga bytes than a
data warehouse, they can be deployed.

Data Mining

Data mining is a major use of data warehouse databases. It is an information-analysis tool
that involves the automated discovery of hidden patterns and trends in historical business
activity. Data mining’s objective is to extract patterns, trends and rules from data
warehouses to evaluate (i.e. predict or score) proposed business strategies, which in turn
will improve competitiveness, improve profits and transform business processes. With the
help of data mining it is possible to improve customer retention, campaign management
and customer segmentation analysis.

E-commerce is another major opportunity for effective use of data mining.

Example: When online retail web sites launch discount sales, it is difficult to figure out how
many first time customers are likely to come back and buy again. Further finding which
customers are price sensitive and more likely to jump on future sales is difficult to identify.
As a solution for that companies are gathering data on user traffic through their web sites
into databases. These databases are analysed using data mining techniques and based on
the analysis, sales promotions are developed and targeted at specific customers.

Predictive analysis is also an advantage of data mining. It combines historical data with
assumptions about future conditions to predict outcomes of events such as future product
sales.

3.4.3. Business Intelligence

Databases can be used for the purpose of business intelligence closely linked to data mining.
Business Intelligence is the process of gathering enough of the right information in a timely
manner and usable form and analyzing it so that it can have a positive impact on business
strategy, tactics or operations. Business Intelligence turns data into valuable information and
distributes it throughout an enterprise. This information is used by the companies to improve
strategic discussions about which markets to enter, how to select and manage key customer
relationships, how to improve sales promotions etc. There are tools available in the market which facilitates Business Intelligence. These tools (applications) can be found in different categories such as Business planning, Customer Relationhip Management (CRM), Management Information Systems (MIS) etc.

Today a number of companies use the business intelligence approach.

3.4.4. Distributed Databases



A distributed database is a database in which the data may be spread across several databases
connected through telecommunications devices. The user does not need to have knowledge about the physical location of the database. DBMS finds the location of data according to the user
requirement.

Distributed databases give more flexibility to the process of database organizing. With the help of
distributed databases employees in one office has the ability to access and share the data in
another office. This feature may improve effectiveness and the efficiency of the organizations.
However, distributed databases create some additional problems for the companies too. As the
distributed databases allow users to access the data from different sites, controlling the access
and changes to data are sometimes difficult. Therefore, ensuring that the data in an organisation
distributed databases are consistently and concurrently updated is a major challenge of distributed database management.

Further, as the databases depend on telecommunications data transport speed can be slower.
To minimize telecommunications cost, some organizations build a replicated database. A
database that holds a duplicate set of frequently used data. At the beginning of the day the
company sends a copy of important data to each distributed processing location. At the end of the day these sites send the changed data back to update the main database. This process is known as data synchronization.

3.4.5. Online Analytical Processing (OLAP)

Online analytical processing allows users to explore data from a number of different perspectives.

To respond to the dynamic and competitive nature of today’s global business environment, the IS industry has made developments like analytical databases, data marts, data warehouses and
specialised servers and web enabled software products that support Online Analytical Processing
(OLAP).

OLAP enables users to interactively examine and manipulate large amounts of detailed and
consolidated data from many perspectives. OLAP involves analysing complex relationships among thousands or even millions of data items stored in data marts, data warehouses, and other multidimensional databases to discover patterns, trends and exceptional conditions. An OLAP session takes place online in real time with rapid response to a manager’s or analyst’s queries, so that their analytical or decision making process is undisturbed.

3.4.6. Object-oriented & Object-Relational Database Management System

Object Database Management Systems extend the object programming language with
transparently persistent data, concurrency control, data recovery, associative queries and other
database capabilities.

An initial area of focus by several object-oriented database vendors has been the Computer Aided Design (CAD), Computer Aided Manufacturing (CAM) and Computer Aided Software Engineering (CASE) applications. A primary characteristic of these applications is the need to manage very complex information efficiently. The most significant benefit of the OODBMS is that these databases have extended into areas not known by the RDBMS. Medicine, multimedia, and highenergy physics are just a few of the new industries relying on object-oriented databases.
As with the relational database method, object-oriented databases also have disadvantages or
limitations. One disadvantage of OODBMS is that it lacks a common data model. There is also no
current standard, since it is still considered to be in the development stages.

ORDBMS is a database management system that provides both relational and objects database
management capabilities. ORDBMS was created to handle new types of data such as audio,
video, and image files that relational databases were not equipped to handle. ORDBMS is a
relational database management system that allows developers to integrate the database with
their own custom data types and methods.

Relational databases store data in tables that have to be joined together to answer complex
queries and this can take considerable processing resources and time. But object databases let
users link complicated data structures as more easily accessible objects. Because of the efficiency
provided by the object-oriented databases they are widely used around the world.

3.4.7. Visual, Audio and Other Database Systems

Apart from raw data, visual and audio signals also need to be stored in organizations.
Example: Credit card companies input pictures of charge slips into an image database using
a scanner Physicians use image databases to store x-rays Music companies store and manipulate sound from recording studios.

Visual databases can be stored in some object –relational databases or special-purpose database
systems.

Combining and analyzing data from separate and totally different databases is an increasingly
important database challenge. Global businesses need to analyze sales and accounting data
stored around the world in different database systems.

Other Database Systems include virtual database systems which integrate data from different
databases from around the world that use different file formats and types.