Page 250 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining Sartaj Singh, Lovely Professional University

notes unit 13: Metadata and Data Warehouse Quality

contents
Objectives
Introduction
13.1 Representing and Analyzing Data Warehouse Quality
13.1.1 Data Warehouse Structure
13.1.2 Importing Data to the Data Warehouse

13.1.3 Preparing Data for Analysis with OLAP Server
13.1.4 Analyzing your Data
13.2 Quality Analysis in Data Staging
13.2.1 The Data Staging Process
13.2.2 Pros and Cons of Data Staging

13.3 Summary
13.4 Keywords
13.5 Self Assessment
13.6 Review Questions

13.7 Further Readings

objectives

After studying this unit, you will be able to:
l z Represent and analyse data warehouse quality
l z Know quality analysis in data staging

introduction

Data warehouses are complex systems consisting of many components which store highly
aggregated data for decision support. Due to the role of the data warehouses in the daily business
work of an enterprise, the requirements for the design and the implementation are dynamic
and subjective. Therefore, data warehouse design is a ontinuous process which has to reflect the
changing environment of a data warehouse, i.e. the data warehouse must evolve in reaction to the
enterprise’s evolution. Based on existing meta models for the architecture and quality of a data
warehouse, we propose in this paper a data warehouse process model to capture the dynamics of
a data warehouse. The evolution of a data warehouse is represented as a special process and the
evolution operators are linked to the corresponding architecture components and quality factors
they affect. We show the application of our model on schema evolution in data warehouses and
its consequences on data warehouse views. The models have been implemented in the metadata
repository Concept-Base which can be used to analyze the result of evolution operations and to
monitor the quality of a data warehouse.

244 LoveLy professionaL university