Hierarchical De-normalizationBy Kamal Panhwar (A1KW-410-435)

In instructions of Sir Muhammad Hussain Mughal

Contents

Introduction and writers intro

Normalization and De-normalization

Methodology

Single Dimensional Queries

Conclusion

Multi Dimensional Quires

Introduction to Topic Two most common processes in database design

are data Normalization and De-normalization, this research discuss opimization using Hierarchical Denormalization

Mortez Zaker is a research student in Advance Databases and data warehouse, he is System Analyst and specialized in database.

Somnuk Phon-Amnuaisuk done B.Eng and have done Ph.D in Artificial Intelligence.

Dr.Su-Cheng Haw’s has research in XML Database. Her favorite fields Data Modeling, Design, Data Management, Data Sestamatic etc.

Introduction Datawarehouse is basically foundation for Decision

Spport Systems, which involve huge collection of information, extractions of data from different sources which is available for On-line Analytical processing (OLAP)

DW involve number of join operations, incurring computational overhead, multi-dimensional grouping and aggregation Operations

Due to nature of DW Performance and efficiency are most required qualities.

Researcher compared the results of Hierarchical De-normalization and how much it effect performance.

Normalization

2. De-normalization Database is transferred from Normalized to

De-normalized to create data ware house.

1. Normalization Normalization is technique in which we

reformat and design database and break in in

such way that it can give us more speed and

integrity constraints and restriction of business

rules.

2003 2004 2005 2006

30

50

70

120

Data Warehouse

Hierachy

Mardan

Pakistan

Peshwar

Frontier

Multan

Punjab

Qasa Khawani

Gulberg

Street

Defence

Lahore

Each member in a hierarchy is Known as a “node”. The top node is called rootAnd bottom nodes are leafs notes. A parentNode is a node that has childrenAnd a child node is a node which Belongs to a A parent

MethodologyResearcher used to compare efficiency of De-Normilzation and normalization processes and analysis the performance of data models, used series of queries on some column for evaluation.

Normalized data set

De-Normalized data set

Query Set

Selection

Business

Evaluation

A set of query Benchmark has been used for frequent query application like Star-Schema in the data warehouse

Queries designed on based business analysis missions.

Evaluation was performed by using calculating time using all queries in benchmark

Query

Record Results

One dimensional Queries

Multi dimensional Queries

Conclusion

Researcher used their methodology and research to know exactly what is effect of using hierarchical de-normalization. The come to following conclusion

The findings confirm that most probably hierarchical denormalization have the capability of improving query perfromance since they can reduce the query response times when the data structure in Data Warehouse is engaged in several joins operations. The result can help researcher in the future to develop general guidelines which can be applicable to majority of database designs.