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Data warehouse and Business
Intelligecne
Characteristics, Features, D- Analysis and
Implementation.
OLAP characteristics
 The need for more intensive decision support prompted the
introduction of a new generation of tools.
 These tools create an advanced data analysis environment that
supports
 decision making,
 business modelling, and
 operations research.
OLAP characteristics - contd
 The followings are the main characteristics of OLAP

They use multidimensional data analysis techniques. - data are
processed and viewed as part of a multidimensional structure.This
type of data analysis is particularly attractive to business decision
makers because they tend to view business data as data that are
related to other business data.

They provide advanced database support. – should support
DBMS, flat files, internal and external Data , supports drill and roll
up.
OLAP Characteristics - Contd
The followings are the main characteristics of OLAP


They provide easy-to-use end-user interfaces. - Advanced
OLAP features become more useful when access to them is kept simple.
OLAP tools have equipped their sophisticated data extraction and
analysis tools with easy-to-use graphical interfaces

They support the client/server architecture - Client/server
architecture provides a framework within which new systems can be
designed, developed, and implemented
OLAP – General features
OLAP – Dimensional Analysis
 Take a simple Star Schema as Product, Time and Store are




the dimensions.
Take Product as in X –axis, Time in Y –axis and Stores in Z –
axis.
Here time is one dimension and month is one of the attribute
of the time dimensions and it is represented in Y – axis.
Similarly the attributes of the product name and store name
is represented in other axis.
This is called Dimension model.
OLAP – dimensional Analysis - contd
Cubes represents, values of attributes along the primary edges of the
cube
OLAP - Implementation
 Before going for the OLAP implementation there are two
issues
 Lack of Standardization
 Scalability
 OLAP is generally good for handling Summarized data, not
good for volumes of detailed data.
OLAP – Implementation - contd
 Implementation Steps
 Dimension Modeling.
 Design and building the MDDB
 Selection of data to be moved to OLAP systems
 Data acquisition and extraction for OLAP systems.
 Data Loading into OLAP server
 Computation of data aggregation and derived data.
 Implementation of application on the desktop.
 Training the user