Page 263 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 14: Quality Driven Data Warehouse Design
Sartaj Singh, Lovely Professional University

unit 14: Quality Driven Data Warehouse Design notes

contents
Objectives
Introduction
14.1 Quality Driven Data Warehouse Design
14.2 Interaction between Quality Factors and DW Tasks
14.2.1 Expected Results and Innovations

14.2.2 Quality Factors and Properties
14.3 The DWQ Data Warehouse Design Methodology
14.3.1 Data Warehouses, OLTP, OLAP and Data Mining
14.3.2 A Data Warehouse Supports OLTP
14.3.3 OLAP is a Data Warehouse Tool

14.3.4 Data Mining is a Data Warehouse Tool
14.3.5 Designing a Data Warehouse: Prerequisites
14.3.6 Data Warehouse Users
14.4 Optimizing and Materialization of DW Views

14.5 Summary
14.6 Keywords
14.7 Self Assessment
14.8 Review Questions
14.9 Further Readings

objectives

After studying this unit, you will be able to:
l z Describe quality driven data warehouse design

l z Know interaction between quality factors and data warehouse tasks
l z Explain DWQ data warehouse design methodology
l z Describe optimizing the materialization of DW views

introduction

A data warehouse (DW) can be seen as a set of materialized views defined over remote base
relations. When a query is posed, it is evaluated locally, using the materialized views, without
accessing the original information sources. The DWs are dynamic entities that evolve continuously
over time. As time passes, new queries need to be answered by them. Some of these queries can
be answered using exclusively the materialized views. In general though new views need to be
added to the DW.

LoveLy professionaL university 257