a comparison of data warehouse design models

8/11/2019 A Comparison of Data Warehouse Design Models

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A COMPARISON OF DATA WAREHOUSE DESIGN MODELS

A MASTERS THESIS

in

Computer Engineering

Atilim University

by

BERIL PINAR BAARAN

J ANUARY 2005


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A THESIS SUBMITTED TO

THE GRADUATE SCHOOL OF NATURAL AND APPL IED SCIENCES

OF

ATILIM UNIVERSITY

BY

BERIL PINAR BAARAN

IN PARTIAL FULFILLMENT OF THE REQ UIREMENTS FOR THE

DEGREE OF

MASTER OF SCIENCE

IN

THE DEPARTMENT OF COMPUTER ENGINEERING

J ANUARY 2005


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Approval of the Graduate School of Natural and Applied Sciences

_____________________

Prof. Dr. Ibrahim Akman

Director

I certify that this thesis satisfies all the requirements as a thesis for the degree of Master

of Science.

_____________________

Prof. Dr. Ibrahim Akman

Head of Department

This is to certify that we have read this thesis and that in our opinion it is fully adequate,in scope and quality, as a thesis for the degree of Master of Science.

_____________________ _____________________

Prof. Dr. Ali Yazici Dr. Deepti Mishra

Co-Supervisor Supervisor

Examining Committee Members

Prof. Dr. Ali Yazici _____________________

Dr. Deepti Mishra _____________________

Asst. Prof. Dr. Nergiz E. altay _____________________

Dr. Ali Arifolu _____________________

Asst. Prof. Dr. idem Turhan _____________________


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ABSTRACT


Baaran, Beril Pnar

M.S., Computer Engineering Department

Supervisor: Dr. Deepti Mishra

Co-Supervisor: Prof. Dr. Ali Yazici

January 2005, 90 pages

There are a number of approaches in designing a data warehouse both in conceptual

and logical design phases. The generally accepted conceptual design approaches are

dimensional fact model, multidimensional E/R model, starER model and object-oriented

multidimensional model. And in the logical design phase, flat schema, terraced schema,

star schema, fact constellation schema, galaxy schema, snowflake schema, star cluster

schema and starflake schemas are widely used approaches. This thesis proposes a

comparison of both the conceptual and the logical design models and a sample data

warehouse design and implementation is provided. It is observed that in the conceptual

design phase, object-oriented model provides the best solution and for the logical design

phase, star schema is generally the best in terms of performance and snowflake is

generally the best in terms of redundancy.

Keywords: Data Warehouse, Design Methodologies, DF, starER, ME/R, OOMD,

flat schema, terraced schema, star schema, fact constellation schema, galaxy schema,

snowflake schema, star cluster schema, starflake schema, DTS, Data Analyzer


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Z

VER AMBARI TASARIM MODELLER KARILATIRMASI

Baaran, Beril Pnar

Yksek Lisans, BilgisayarMhendislii Blm

Tez Yneticisi: Dr. Deepti Mishra

Ortak Tez Yneticisi: Prof. Dr. Ali Yazici

Ocak 2005, 90 sayfa

Veri ambar tasarmnn kavramsal ve mantksal tasarm aamalar iin birden fazla

yaklam vardr. Kavramsal tasarm safhas iin genelolarak kabul grm yaklamlar

dimensional fact, multidimensional E/R, starER ve object-oriented

multidimensional modelleridir. Mantksal tasarm safhas iin genel olarak kabul

grm yaklamlar flat, terraced, star, fact constellation, galaxy ,

snowflake, star cluster ve starflake emalardr. Bu tez, kavramsal ve mantksal

tasarm modellerini karlatrr, rnek bir veri ambar tasarmn ve uygulamasn ierir.

Bu tezde, kavramsal tasarm aamasnda object-oriented multidimensional modelinin;

mantksal tasarm aamasnda performanskriteri asndan star emann, veri tekrar

kriteri asndan snowflake emann en iyi zmler olduu gzlendi.

Anahtar Kelimeler: VeriAmbar, Tasarm Yntemleri, DF, starER, ME/R, OOMD, flat

ema, terraced ema, star ema, fact constellation ema, galaxy ema, snowflake ema,

star cluster ema, starflake ema, DTS, Data Analyzer


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To my dear husband

Thanks for his endless support


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ACKNOWLEDGEMENTS

First, I would like to thank my thesis advisor Dr. Deepti MISHRA and co-

supervisor Prof. Dr. Ali YAZICI for their guidance, insight and encouragement

throughout the study.

I should also express my appreciation to examination committee members Asst.

Prof. Dr. Nergiz E. AILTAY, Dr. Ali ARIFOLU, Asst. Prof. Dr. idem

TURHAN for their valuable suggestions and comments.

I would like to express my thanks to my husband for his assistance, encouragement

and all members of my family for their patience, sympaty and support during the study.


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TABLE OF CONTENTS

ABSTRACT .......................................................................................................................... iii

Z........................................................................................................................................... iv

ACKNOWLEDGEMENTS.................................................................................................. vi

TABLE OF CONTENTS.....................................................................................................vii

LIST OF TABLES .................................................................................................................x

LIST OF FIGURES...............................................................................................................xi

LIST OF ABBREVIATIONS ............................................................................................xiii

CHAPTER

1 INTRODUCTION.............................................................................................................. 1

1.1. Scope and outline of the thesis...................................................................................2

2 DATA WAREHOUSE CONCEPTS ................................................................................. 3

2.1. Definition of Data Warehouse ...................................................................................3

2.2. Why OLAP systems must run with OLTP................................................................ 5

2.3. Requirements for Data Warehouse Database Management Systems......................8

3 FUNDAMENTALS OF DATA WAREHOUSE............................................................10

3.1. Data acquisition......................................................................................................... 12

3.1.1. Extraction, Cleansing and Transformation Tools ............................................13

3.2. Data Storage and Access .......................................................................................... 13

3.3. Data Marts ................................................................................................................. 14

4 DESIGNING A DATA WAREHOUSE.......................................................................... 164.1. Beginning with Operational Data ............................................................................16

4.2. Data/Process Models ................................................................................................ 18

4.3. The DW Data Model ................................................................................................ 19

4.3.1. High-Level Modeling ........................................................................................ 19

4.3.2. Mid-Level Modeling ......................................................................................... 21


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4.3.3. Low-Level Modeling......................................................................................... 23

4.4. Database Design Methodology for DW .................................................................. 24

4.5. Conceptual Design Models ...................................................................................... 27

4.5.1. The Dimensional Fact Model............................................................................ 27

4.5.2. Multidimensional E/R Model ...........................................................................30

4.5.3. starER ................................................................................................................. 33

4.5.4. Object-Oriented Multidimensional Model (OOMD) ......................................35

4.6. Logical Design Models............................................................................................. 36

4.6.1. Dimensional Model Design .............................................................................. 37

4.6.2. Flat Schema........................................................................................................ 39

4.6.3. Terraced Schema ............................................................................................... 40

4.6.4. Star Schema........................................................................................................ 414.6.5. Fact Constellation Schema................................................................................ 43

4.6.6. Galaxy Schema ..................................................................................................43

4.6.7. Snowflake Schema ............................................................................................44

4.6.8. Star Cluster Schema .......................................................................................... 45

4.6.9. Starflake Schema ............................................................................................... 47

4.6.10. Cube.................................................................................................................. 48

4.7. Meta Data ..................................................................................................................53

4.8. Materialized views....................................................................................................53

4.9. OLAP Server Architectures .....................................................................................54

5 COMPARISON OF MULTIDIMENSIONAL DESIGN MODELS.............................56

5.1. Comparison of Dimensional Models and ER Models ............................................56

5.2. Comparison of Dimensional Models and Object-Oriented Models ......................57

5.3. Comparison of Conceptual Multidimensional Models........................................... 58

5.4. Comparison of Logical Design Models...................................................................60

5.5. Discussion on Data Warehousing Design Tools..................................................... 61

6 IMPLEMENTING A DATA WAREHOUSE.................................................................64

6.1. A Case Study............................................................................................................. 64

6.2. OOMD Approach...................................................................................................... 65

6.3. starER Approach ....................................................................................................... 68


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6.4. ME/R Approach ........................................................................................................ 70

6.5. DF Approach ............................................................................................................. 72

6.6. Implementation Details............................................................................................. 74

7 CONCLUSIONS AND FUTURE WORK...................................................................... 83

7.1. Contributions of the Thesis ...................................................................................... 85

7.2. Future Work .............................................................................................................. 86

REFERENCES ..................................................................................................................... 87


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LIST OF TABLES

TABLE

2.1 Comparison of OLTP and OLAP.................................................................................... 7

4.1 2-dimensional pivot view of an OLAP Table .............................................................49

4.2 3-dimensional pivot view of an OLAP Table .............................................................49

5.1 Comparison of ER, DM and OO methodologies ......................................................... 585.2 Comparison of conceptual design models....................................................................60

5.3 Comparison of logical design models...........................................................................61


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LIST OF FIGURES

FIGURE

2.1 Consolidation of OLTP information ............................................................................... 4

2.2 Same attribute with different formats in different sources............................................4

2.3 Simple comparison of OLTP and DW systems .............................................................5

3.1 Architecture of DW........................................................................................................ 10

4.1 Data Extract ion............................................................................................................... 16

4.2 Data Integration .............................................................................................................. 17

4.3 Same data, different usage............................................................................................. 17

4.4 A Simple ERD for a manufacturing environment........................................................ 20

4.5 Corporate ERD created by departmental ERDs........................................................... 20

4.6 Relationship between ERD and DIS............................................................................. 21

4.7 Midlevel model members .............................................................................................. 21

4.8 A Midlevel model sample.............................................................................................. 224.9 Corporate DIS formed by departmental DISs. .............................................................23

4.10 An example of a departmental DIS.............................................................................23

4.11 Considerations in low-level modeling ........................................................................ 24

4.12 A dimensional fact schema sample............................................................................. 28

4.13 The graphical notation of ME/R elements.................................................................. 31

4.14 Multiple cubes sharing dimensions on different levels .............................................32

4.15 Combining ME/R notations with E/R.........................................................................33

4.16 Notation used in starER.............................................................................................. 33

4.17 A sample DW model using starER .............................................................................35

4.18 Flat Schema ................................................................................................................. 40

4.19 Terraced Schema......................................................................................................... 41

4.20 Star Schema................................................................................................................. 42


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4.21 Fact Constellation Schema ......................................................................................... 43

4.22 Galaxy Schema............................................................................................................ 44

4.23 Snowflake Schema ...................................................................................................... 45

4.24 Star Schema with fork .............................................................................................. 46

4.25 Star Cluster Schema ....................................................................................................47

4.26 Starflake Schema......................................................................................................... 47

4.27 Comparison of schemas............................................................................................... 48

4.28 3-D Realization of a Cube ...........................................................................................50

4.29 Operations on a Cube................................................................................................... 52

6.1 ER model of sales and shipping systems...................................................................... 65

6.2 Use case diagram of sales and shipping system........................................................... 66

6.3 Statechart diagram of sales and shipping system......................................................... 676.4 Static structure diagram of sales and shipping system ................................................ 67

6.5 Sales subsystem starER model..................................................................................... 69

6.6 Shipping subsystem starER model................................................................................ 70

6.7 Sales subsystem ME/R model ....................................................................................... 71

6.8 Shipping subsystem ME/R model................................................................................. 72

6.9 Sales subsystem DF model............................................................................................73

6.10 Shipping subsystem DF model.................................................................................... 73

6.11 Snowflake schema for the sales subsystem............................................................... 74

6.12 Snowflake schema for the shipping subsystem.........................................................75

6.13 General architecture of the case study ........................................................................ 75

6.14 Sales DTS Package ...................................................................................................... 77

6.15 Shipping DTS Package ................................................................................................ 77

6.16 Transformation details for delimited text file ........................................................... 78

6.17 Transact-SQL query as the transformation source.................................................... 79

6.18 Pivot Chart using Excel as client ............................................................................... 80

6.19 Pivot Table using Excel as client ............................................................................... 80

6.20 Data Analyzer as client ............................................................................................... 81


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LIST OF ABBREVIATIONS

3GL - Third Generation Language

4GL - Fourth Generation Language

DAG - Directed Acyclic Graph

DB - Database

DBMS - Database Management Systems

DDM - Data Dimensional Modeling

DF - Dimensional Fact

DIS - Data Item Set

DSS - Decision Support System

DTS - Data Transformation Services

DW - Data Warehouse

ER - Entity RelationshipERD - Entity Relationship Diagram

ETL - Extract, Transform, Load

HOLAP - Hybrid OLAP

I/O - Input/Output

IT - Information Technology

ME/R - Multidimensional E/R

MOLAP - Multidimensional OLAP

ODBC - Open Database Connectivity

OID - Object Identifier

OLAP - Online Analytical Processing

OLTP - Online Transaction Processing

OO - Object Oriented


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OOMD - Object Oriented Multidimensional

RDBMS - Relational Database Management Systems

ROLAP - Relational OLAP

SQL - Structured Query Language

UML - Unified Modeling Language

XML - Extensible Markup Language


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CHAPTER 1

INTRODUCTION

Information is an asset that provides benefit and competitive advantage to any

organization. Today, every corporation have a relational database management system

that is used for organizations daily operations. The companies desire to increase the

value of their organizational data by turning it into actionable information. As the

amount of the organizational data increases, it becomes harder to access and get the most

information out of it, because it is in different formats, exists on different platforms and

resides on different structures. Organizations have to write and maintain several

programs to consolidate data for analysis and reporting. Also, the corporate decision-

makers require access to all the organizations data at any level, which may mean

modifications on existing or development of new consolidation programs. This process

would be costly, inefficient and time consuming for an organization.

Data warehousing provides an excellent approach in transforming operational data

into useful and reliable information to support the decision making process and also

provides the basis for data analysis techniques like data mining and multidimensional

analysis. Data warehousing process contains extraction of data from heterogenous data

sources, cleaning, filtering and transforming data into a common structure and storing

data in a structure that is easily accessed and used for reporting and analysis purposes.


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As the need for building an organizational data warehouse is clear, now the

question is how. There are generally accepted design methodologies in designing and

implementing a data warehouse. The focus of this thesis is discussing the data

warehouse conceptual and logical design models and comparing these approaches.

1.1.Scope and outline of the thesis

The thesis organized as follows: Chapter 2 presents an overview of data warehouse

concepts and makes a comparison between operational and analytical processing

systems. Chapter 3 provides information on data warehousing fundamentals and process.

Chapter 4 gives information on data warehouse design approaches used in conceptual

and logical design phases. In chapter 5, the design approaches described in chapter 4 are

discussed and compared. Finally in chapter 6, a sample conceptual model is logicallyimplemented using the logical design models and the physical implementation of a data

warehouse is described.


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CHAPTER 2

DATA WAREHOUSE CONCEPTS

2.1.Definition of Data Warehouse

A data warehouse (DW)refers to a database that is different from the organizations

Online Transaction Processing (OLTP) database and that is used for the analysis of

consolidated historical data.

According to Barry Devlin, IBM Consultant, a DW is simply a single, complete

and consistent store of data obtained from a variety of sources and made available to endusers in a way they can understand and use it in a business context[1, 3].

According to W.H. Inmon, a DW is a subject-oriented , integrated , time-variant,

and nonvolatile collection of data in support of managements decision making process

[1, 2, 3, 6, 10, 11].

The description of the four key features of the DW is given below.

Subject-oriented: In general, an enterprise contains information that is very detailed to

meet all requirements needed for related subsets of the organization (sales dept, humanresources dept, marketing dept etc.) and optimized for transaction processing. Usually,

this type of data is not suitable for decision-makers to use. Decision-makers need

subject-oriented data. DW should include only key business information. The data in the

warehouse should be organized based on subject and only subject-oriented data should

be moved into a warehouse.


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If the decision-maker needs to find all information about a spesific product, he/she

would need to use all systems like rental sales system, order sales system and catalog

sales system, which is not the preferable and the practical way. Instead, all the key

information must be consolidated in a warehouse and organized into subject areas as

illustrated in Figure 2.1.

Figur e 2.1 Consolidation of OLTP information

Integrated: DW is an architecture constructed by integrating data from multiple

heterogeneous sources (like relational database (DB), flat files, excel sheets, XML data,

data from the legacy systems) to support structured and/or ad hoc queries, analytical

reporting and decision making. DW also provides mechanisms for cleaning and

standardizing data. Figure 2.2 emphasizes various uses and formats of Product Codeattribute.

Figur e 2.2 Same attribu te with different formats in d ifferent sources

Time-variant: DW provides information from a historical prospective. Every keystructure in the DW contains, either implicitly or explicitly, an element of time. A DW

generally stores data that is 5-10 years old, to be used for comparisons, trends and

forecasting.

Nonvolatile: Data in the warehouse are not updated or changed (see Figure 2.3), so it

does not require transaction processing, recovery and concurrency control mechanisms.


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The operations needed in the DW are initial loading of data and access of data and

refresh.

Figur e 2.3 Simple compar ison of OLTP and DW systems

Some of the DW characteristics are given below;

It is a database that is maintained separately from organizations operational

databases.

It allows for integration of various application systems.

It supports information processing by consolidating historical data.

User interface aimed at decision-makers.

It contains large amount of data.

It is updated infrequently but periodically updates are required to keep the

warehouse meaningful and dynamic.

It is subject-oriented.

It is non-volatile.

Data is longer-lived. Transaction systems may retain data only until processing is

complete, whereas data warehouses may retain data for years.

Data is stored in a format that is structured for querying and analysis.

Data is summarized. DWs usually do not keep as much detail as transaction-

oriented systems.

2.2.

Why OLAP systems must r un with OLTP

In this section, I aim to make a comparison of OLTP and Online Analytical

Processing (OLAP) systems and explain the reasons why an OLAP system is needed.


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The nature of OLTP and OLAP systems are completely different both in technical

and in business needs.

The following table compares OLTP systems OLAP systems in main technical

topics

OLTP OLAP

User and System

Orientation

Thousands users, customer-

oriented, used for

transactions and querying

clerks, clients and

Information Technology

(IT) professionals

Hundreds users, market-

oriented, used for data

analysis by knowledge

workers

Data Contents Manages current data, very

detail-oriented

Manages large amounts of

historical data, provides

facilities for summarization

and aggregation, stores

information at different

levels of granularity to

support decision makingprocess

Data is continuously

updated

Data is refreshed

Data is volatile and

normalized (Entity-

Relationship (ER) Model)

Data is non-volatile and de-

normalized (Dimensional

Model)

Database Design Adopts an ER model and an

application-oriented

database design, index/hash

on primary key.

Adopts star, snowflake, or

fact constellation model

and a subject-oriented

database design, lots of

scans.


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OLTP and OLAP systems need to run different types of queries. They may

provide different functionality and use different types on queries.

The main roles in a company that will use a DW solution are [4];

Top executives and decision makers

Middle/operational managers

Knowledge workers

Non-technical business related individuals

The main advantages of using a DW solution are summarized in the list below [2, 3, 6];

High query performance

Does not interfere with local processing at sources

Information copied at warehouse (can modify, summarize, restructure, etc.)

Potential high Return on Investment

Competitive advantage

Increase productivity of corporate decision makers

As discussed above, a DW solution has many advantages and benefits to an

organization. Also implementing a DW solution solves some business problems, it may

bring some new self-owned problems mentioned below [2, 6];

Underestimation of resources for data loading

Hidden problems with source systems

Required data not captured

Increased end-user demands

High maintenance

Long duration projects

Complexity of integration

Data homogenization

High demand for resources

Data ownership

2.3.

Requirements for Data War ehouse Database Management Systems

In the implementation of a DW solution, many technical points must be considered.

While an OLTP database management systems (DBMS) must only consider transaction


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processing performance (which is basically; a transaction must be completed in the

minimum time; without deadlocks; and with support of thousands of transactions per

second)

The relational DBMS (RDBMS) suitable for data warehousing has the following

requirements [6];

Load performance: Data warehouses need incremental loading of data

periodically so the load process performance should be like gigabytes of data per

hour.

Load processing:Data conversion, filtering, indexing and reformatting may be

necessary during loading data into the data warehouse. This process should be

executed as a single unit of work.

Data quality management: The warehouse must ensure consistency and

referential integrity despite various data sources and big data size. The measure

of success for a data warehouse is the ability to satisfy business needs.

Query Performance: Complex queries must complete in acceptable periods.

Terabyte scalability: The data warehouse RDBMS should not have any

database size limitations and should provide recovery mechanisms.

Mass user scalability: The data warehouse RDBMS should be able to support

hundreds of concurrent users.

Warehouse administration: Easy-to-use and flexible administrative tools

should exists for data warehouse administration.

Advanced query functionality: The data warehouse RDBMS should supply

advanced analytical operations to enable end-users perform advanced

calculations and analysis.


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CHAPTER 3

FUNDAMENTALS OF DATA WAREHOUSE

The main reason for building a DW is to improve the quality of information in the

organization. Data coming from both internal and external sources in various formats

and structures is consolidated and integrated into a single repository. DW system

comprises the data warehouse and all components used for building, accessing and

maintaining the data warehouse.

Figure 3.1 Architecture of DW


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A general architecture of a DW is given in Figure 3.1 and the main components are

described below [5, 32].

The data import and preparation component is responsible for data acquisition. It

includes all programs (like Data Transformation Services (DTS)) that are responsible for

extracting data from operational sources, preparing and loading it into the warehouse.

The access component includes all applications (like OLAP) that use the

information stored in the warehouse.

Additionally, a metadata management component is responsible for the

management, definition and access of all different types of metadata. Metadata is

defined as data describing the meaning of data. In data warehousing, there are various

types of metadata, e.g., information about the operational sources, the structure andsemantics of the data warehouse data, the tasks performed during the construction, the

maintenance and access of a data warehouse, etc.

Implementing a DW is a complex task containing two major phases. In the

configuration phase, a conceptual view of the warehouse is first specified according to

user requirements (DW design). Then, the related data sources and the Extraction-Load-

Transform (ETL) process (data acquisition) are determined. Finally, decisions about

persistent storage of the warehouse using database technology and the various ways datawill be accessed during analysis are made.

After the initial load (the first load of the DW according to the configuration),

during the operation phase, warehouse data must be regularly refreshed, i.e.,

modifications of operational data since the last DW refreshment must be propagated into

the warehouse such that data stored in the data warehouse reflect the state of the

underlying operational systems.

A more natural way to consider multidimensionality of warehouse data is providedby the multidimensional data model. In this model, the data cube is the basic modeling

construct. Operations like pivoting (rotate the cube), slicing-dicing (select a subset of the

cube), roll-up and drill-down (increasing and decreasing the level of aggregation) can be

applied to a data cube. For the implementation of multidimensional databases, there are

two main approaches. In the first approach, extended RDBMSs, called relational OLAP


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(ROLAP) servers, use a relational database to implement the multidimensional model

and operations. ROLAP servers provide SQL extensions and translate data cube

operations to relational queries. In the second approach, multidimensional OLAP

(MOLAP) servers store multidimensional data in non-relational specialized storage

structures. These systems usually precompute the results of complex operations (during

storage structure building) in order to increase performance.

3.1.

Data acquisition

Data extraction is one of the most time-consuming tasks of DW development. Data

consolidated from heterogenous systems may have problems, and may need to be first

transformed and cleaned before loaded into the DW. Data gathered from operational

systems may be incorrect, inconsistent, unreadable or incomplete. Data cleaning is anessential task in data warehousing process in order to get correct and qualitative data

into the DW. This process contains basically the following tasks: [5]

converting data from heterogenous data sources with various external

representations into a common structure suitable for the DW

identifying and eliminating redundant or irrelevant data

transforming data to correct values (e.g., by looking up parameter usage and

consolidating these values into a common format) reconciling differences between multiple sources, due to the use of homonyms

(same name for different things), synonyms (different names for same things) or

different units of measurement

As the cleaning process is completed, the data that will be stored in the warehouse

must be merged and set into a common detail level containing time related information

to enable usage of historical data. Before loading data into the DW, tasks like filtering,

sorting, partitioning and indexing may need to be performed. After these processes, the

consolidated data may be imported into the DW using one of bulk data loaders, a custom

application or an import/export wizard provided by the DBMS administration

applications.


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3.1.1.Extr action, Cleansing and Transformation Tools

The tasks of capturing data from a source system, cleansing, and transforming the

data and loading the consolidated data into a target system can be done either by

separate products or by a single integrated solution. Integrated solutions fall into one of

the following categories [6]:

Code generators

Database data replication tools

Dynamic transformation engines

There are solutions that fulfill all of the requirements mentioned above. One of these

products is Microsoft Data Transformation Services is described in chapter 6.

Code generators

Code generators create customized 3GL, 4GL transformation programs based on source

and target data definitions. The main issue with this approach is the management of the

large number of programs required to support a complex corporate DW.

Database data r eplication tools

Database data replication tools employ database triggers or a recovery log to capture

changes to a single data source on one system and apply the changes to a copy of the

source data located on a different system. Most replication products dont support the

capture of changes to non-relational files and databases and often not provide facilities

for significant data transformation and enhancement. These tools can be used to rebuild

a database following failure or to create a database for a data mart, provided that the

number of data sources is small and the level of data transformation is relatively simple.

Dynamic tr ansformation engines

Rule-driven dynamic transformation engines capture data from a source system at user-

defined intervals, transform the data and then send and load the results into a target

environment. Most products support only relational data sources, but products are nowemerging that handle non-relational source files and databases.

3.2.Data Storage and Access

Because of the special nature of warehouse data and access, accustomed

mechanisms for data storage, query processing and transaction management must be


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adapted. DW solutions need complex querying requirements and operations involving

large volumes of data access. These operations need special access methods, storage

structures and query processing techniques.

The storage approaches of a DW is described in detail in section 4.9. One of these

physical storage methods may be chosen concerning the trade-off between query

performance and amount of data.

Once the DW is available for end-users, there are a variety of techniques to enable

end-users access the DW data for analysis and reporting. There are several tools and

products that are commercially available. In common all client tools use generally

OLEDB, ODBC or native client providers to access the DW data. The most

commercially used client application is Microsoft Excel with pivot tables.

A company that makes business in several countries througout the world may need

to analyse regional trends and my need to compete in regions. A centric DW may not be

feasible for these companies. These organizations may need to establish data marts

which are selected parts of the DW that support specific decision support application

requirements of a companys department or geographical region. Data marts usually

contain simple replicas of warehouse partitions or data that has been further summarized

or derived from base warehouse data. Data marts allow the efficient execution of

predicted queries over a significantly smaller database.

3.3.Data Marts

A data mart is a subset of the data in a DW and is summary data relating to a

department or a specific function [6]. Data marts focus on the requirements of users in a

particular department or business function of an organization. Since data marts are

specialized for departmental operations, they contain less data and the end-users are

much capable of exploiting data marts than DWs. The main reasons for implementing adata mart instead of a DW may be summarized as follows:

Data marts enable end-users to analyze the data they need most often in their

daily operations.


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Since data marts contain less data, the end-user response time in queries is much

quicker.

Data marts are more specialized and contain less data, therefore data

transformation and integration tasks are much faster in data marts than DWs and

setting up a data mart is a simpler and a cheaper task compared to establishing an

organizational DW in terms of time and resources.

In terms of software engineering, building a data mart may be a more feasible

project than building a DW, because the requirements of building a data mart are

much more explicit than a corporate wide DW project.

Although data marts seem to have advantages over DWs, there are some issues that must

be addressed about data marts.

Size:Although data marts are considered to be smaller than data warehouses, size and

complexity of some data marts may match a small corporate DW. As the size of a data

mart increases, it is likely to have a performance decrease.

Load performance: Both end-user response time and data loading performance are

critical tasks of data marts. For increasing the response time, data marts usually contain

lots of summary tables and aggregations which have a negative effect on load

performance.

User access to data in multiple data marts: A solution to this problem is buildingvirtual data marts which are views of several physical data marts.

Administration: With the increase in number of data marts, the management need

arises to coordinate data mart activities such as versioning, consistency, integrity,

security and performance tuning.


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CHAPTER 4

DESIGNING A DATA WAREHOUSE

Designing a warehouse means to complete all the requirements mentioned in

section 2.3 and obviously is a complicated process.

There are two major components to build a DW; the design of the interface from

operational systems and the design of the DW [11]. DW design is different from a

classical requirements-driven systems design.

4.1.

Beginning with Opera tional DataCreating the DW does not only involve extracting operational data and entering it

into the warehouse (Figure 4.1) .

Figur e 4.1 Data Extraction


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Pulling the data into the DW without integrating it is a big mistake ( Figure 4.2 ).

Figur e 4.2 Data Integration

Existing applications were designed with their own requirements and integration

with other applications was not concerned much. These results in data redundancy, i.e.

same data may exist in other applications with same meaning, with different name or

with different measure ( Figure 4.3 ).

Figur e 4.3 Same data , different usage

Another problem is the performance of accessing existing systems data. The

existing systems environment holds gigabytes and perhaps terabytes of data, and

attempting to scan all of it every time a DW load needs to be done is resource and timeconsuming and unrealistic.

Three types of data are loaded into the DW from the operational system:

Archival data

Data currently contained in the operational environment


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Changes to the DW environment from the updates that have occurred in the

operational system since the last refresh

Five common techniques are used to limit the amount of operational data scanned to

refresh the DW.

Scan data that has been timestamped in the operational environment.

Scan a 'delta' file. A delta file contains only the changes made to an application

as a result of the transactions that have run through the operational environment.

Scan a log file or an audit file created by the transaction processing system. A

log file contains the same data as a delta file.

Modify application code.

Rubbing a 'before' and an 'after' image of the operational file together.

Another difficulty is that operational data must undergo a time-basis shift as it

passes into the DW. The operational datas accuracy is valid at the instant it is accessed,

after that it may be updated. However when the data is loaded into the warehouse, it

cannot be updated anymore, so a time element must be attached to it.

Another problem when passing data is the need to manage the volume of data that

resides in and passes into the warehouse. Volume of data in the DW will grow fast.

4.2.

Data/Process ModelsThe process model applies only to the operational environment. The data model

applies to both the operational environment and the DW environment.

A process model consists:

Functional decomposition

Context-level zero diagram

Data flow diagram

Structure chart State transition diagram

Hierarchical input process output(HIPO) chart

Pseudo code


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A process model is invaluable, for instance, when building the data mart. The

process model is requirements-based; it is not suitable for the DW.

The data model is applicable to both the existing systems environment and the DW

environment. An overall corporate data model has been constructed with no regard for a

distinction between existing operational systems and the DW. The corporate data model

focuses on only primitive data. Performance factors are added into the corporate data

model as the model is transported to the existing systems environment. Although few

changes are made to the corporate data model for operational environment, more

changes are made to the corporate data model to use in DW environment. First, data that

is used purely in the operational environment is removed. Next, the key structures of the

corporate data model are enhanced with an element of time. Derived data is added to the

corporate data model where the derived data is publicly used and calculated once, not

repeatedly. Finally, data relationships in the operational environment are turned into

artifacts in the DW. A final design activity in transforming the corporate data model to

the data warehouse data model is to perform stability analysis. Stability analysis

involves grouping attributes of data together based on their tendency for change.

4.3.The DW Data Model

There are three levels in data modeling process: high-level modeling (called the

ERD, entity relationship level), midlevel modeling (called the data item set, or DIS), and

low-level modeling (called the physical model).

4.3.1.High-Level Modeling

The high level of modeling features entities and relationships. The name of the

entity is surrounded by an oval. Relationships among entities are depicted with arrows.

The direction and number of the arrowheads indicate the cardinality of the relationship,

and only direct relationships are indicated.


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Figur e 4.4 A Simple ERD for a manufacturing environment

The entities that are shown in the ERD level (see Figure 4.4) are at the highest level

of abstraction.

The corporate ERD as shown in Figure 4.5 is formed of many individual ERDs that

reflect the different views of people across the corporation. Separate high-level data

models have been created for different communities within the corporation. Collectively,

they make up the corporate ERD.

Figure 4.5 Corp ora te ERD created by depar tmental ERDs


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4.3.2.Mid-Level Modeling

After the high-level data model is created, the next level is establishedthe

midlevel model or the DIS. For each major subject area, or entity, identified in the high-

level data model, a midlevel model is created. Each area is subsequently developed into

its own midlevel model (see Figure 4.6)

Figur e 4.6 Relationship between ERD and DIS

Four basic constructs are found at the midlevel model (also shown in Figure 4.7):

A primary grouping of data

A secondary grouping of data

A connector, suggesting the relationships of data between major subject areas

Type of data

Figure 4.7 Midlevel model members


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Figur e 4.9 Corporate DIS formed by depar tmental DISs.

Figure 4.10 shows an individual departments DIS.

Figur e 4.10 An example of a depar tmental DIS

4.3.3.

Low-Level Modeling

The physical data model is created from the midlevel data model just by extending

the midlevel data model to include keys and physical characteristics of the model. At

this point, the physical data model looks like a series of tables, sometimes called


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relational tables. With the DW, the first step in doing so is deciding on the granularity

and partitioning of the data.

After granularity and partitioning are factored in, a variety of other physical design

activities are embedded into the design. At the heart of the physical design

considerations is the usage of physical input/output (I/O). Physical I/O is the activity that

brings data into the computer from storage or sends data to storage from the computer.

The job of the DW designer is to organize data physically for the return of the

maximum number of records from the execution of a physical I/O. Figure 4.11 illustrate

the major considerations in low-level modeling.

Figure 4.11 Consider ations in low-level modeling

There is another mitigating factor regarding physical placement of data in the data

warehouse: Data in the warehouse normally is not updated. This frees the designer to use

physical design techniques that otherwise would not be acceptable if it were regularly

updated.

4.4.Database Design Methodology for DW

In the next few sections of this thesis I will be discussing both conceptual and

logical design methods of data warehousing. Adopting the terminology of [23, 36, 37,

38] three different design phases are distinguished; conceptual design manages concepts

that are close to the way users perceive data; logical design deals with concepts related

to a certain kind of DBMS; physical design depends on the specific DBMS and

describes how data is actually stored [35, 40].


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Prior to beginning the discussion, the basic concepts of dimensional modeling

should be mentioned which are: facts, dimensions and measures [7, 24].

A fact is a collection of related data items, consisting of measures and context

data. It typically represents business items or business transactions. A dimension is a collection of data that describe one business dimension.

Dimensions determine the contextual background for the facts; they are the

parameters over which we want to perform OLAP.

A measure is a numeric attribute of a fact, representing the performance or

behavior of the business relative to the dimensions.

Before this discussion, I also prefer to summarize the methodology proposed by

Kimball [21], who is accepted as a guru on data warehousing and whose studies have

encouraged many academicians on the study of data warehousing.

The nine step methodology by Kimball is as follows[6, 42, 43]:

1. Choosing the process: The process (function) refers to the subject matter of a

particular data mart. The first data mart to be built should be the one that is most

likely to be delivered on time with in budget and to answer the most important

business question.

2. Choosing the grain: This means deciding exactly what a fact table record

represents. Only when the grain for the fact table is chosen can we identify the

dimensions of the fact table. The grain decision for the fact table also determines

the grain of each of the dimension tables.

3. Identifying and conforming the dimensions: Dimensions set the context for

asking questions about the facts in the fact table. A well-built set of dimensions

makes the data mart understandable and easy to use. A poorly presented or

incomplete set of dimensions will reduce the usefulness of a data mart to an

enterprise. When a dimension is used in more than one data mart, the dimensionis referred to as being conformed.

4. Choosing the facts : The grain of the fact table determines which facts can be

used in the data mart. All the facts must be expressed at the level implied by the

grain. The facts should be numeric and additive. Additional facts can be added to

a fact table at any time provided they are consistent with the grain of the table.


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5. Storing pre-calculations in the fact table : Once the facts have been selected each

should be re-examined to determine whether there are opportunities to use pre-

calculations.

6. Rounding out the dimension tables : We return to the dimension tables and add

as much text description to the dimensions. The text descriptions should be as

intuitive and understandable to the users. The usefulness of a data mart is

determined by the scope and nature of the attributes of the dimension tables.

7. Choosing the duration of the database: The duration measures how far back in

time the fact table goes. There is requirement to look at the same time period a

year or two earlier. Very large fact tables raise at least two very significant DW

design issues. First, it is often increasingly difficult to source increasingly old

data. The older data, the more likely there will be more problems in reading andinterpreting the old files or the old tapes. Second, it is mandatory that the old

versions of the important dimensions be used, not the most current versions. This

is known as the slowly changing dimension problem.

8. Tracking slowly changing dimensions: There are three basic types of slowly

changing dimensions:

o Type1: where a changed dimension attribute is overwritten,

o Type2: where a changed dimension attribute causes a new dimension

record to be created,

o Type3: a changed dimension attribute causes an alternate attribute to be

created so that both the old and new values of the attribute are

simultaneously accessible in the same dimension record.

9. Deciding the query priorities and the query modes: We consider physical design

issues. The most critical physical design issues affecting the end-users

perception of the data mart are physical sort order of the fact tab le on disk and

the presence of pre-stored summaries or aggregations. There are additional

physical design issues affecting administration, backup, indexing performance,

and security.We have a design for data mart that supports the requirements of a

particular business process and also allows the easy integration with other related

data marts to ultimately form the enterprise-wide DW.


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4.5.Conceptual Design Models

The main goal of conceptual design modeling is developing a formal, complete,

abstract design based on the user requirements [34].

At this phase of a DW there is the need to: Represent facts and their properties: Facts properties are usually numerical and

can be summarized (aggregated).

Connect the dimension to facts: Time is always associated to a fact.

Represent objects and capture their properties with the associations among them:

Object properties (summary properties) can be numeric. Additionally there are

three special types of associations; specialization/generalization (showing objects

as subclasses of other objects), aggregation (showing objects as parts of a layer

object), membership (showing that an object is a member of another higher

object class with the same characteristics and behavior). Strict membership (or

not) (all members belong to only one higher object class), Complete membership

(or not) (all members belong to one higher object class and that object class is

consisted by those members only).

Record the associations between objects and facts: Facts are connected to

objects.

Distinguish dimensions and categorize them into hierarchies: dimensions

governed by associations of type membership forming hierarchies that specify

different granularities.

4.5.1.The Dimensional Fact Model

This model is built from ER schemas [9, 15, 16, 17, 33]. The Dimensional Fact

(DF) Model is a collection of tree structured fact schemas whose elements are facts,

attributes, dimensions and hierarchies. Fact attributes additivity, optional dimension

attributes and non-dimension attributes existence may also be represented on fact

schemas. Compatible fact schemas may be overlapped in order to relate and compare

data.

A fact schema is structured as a tree whose root is a fact. The fact is represented by

a box which reports the fact name.


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Figure 4.12 A dimensional fact schema sample

Sub-trees rooted in dimensions are hierarchies. The circles represent the attributes

and the arcs represent relationship between attribute pairs. The non-dimension attributes

(address attribute as shown in Figure 4.12) are represented by lines instead of circles. Anon-dimension attribute contains additional information about an attribute of the

hierarchy, is connected to it by a -to-one relationship and cannot be used for

aggregation. The arcs represented by dashes express optional relationships between pairs

of attributes.

A fact expresses a many-to-many relationship among the dimensions. Each

combination of values of the dimensions defines a fact instance, one value for each fact

attribute. Most attributes are additive along all dimensions. This means that the sumoperator can be used to aggregate attribute values along all hierarchies. A fact attribute is

called semi-additive if it is not additive along one or more dimensions, non-additive if it

is additive along no dimension.

DF model consists of 5 steps;

Defining facts (a fact may be represented on the E/R schema either by an entity F

or by an n-ary relationships between entities E1 to En).

For each fact;o Building the attribute tree. (Each vertex corresponds to an attribute of the

schema; the root corresponds to the identifier of F; for each vertex v, the

corresponding attribute functionally determines all the attributes

corresponding to the descendants of v. If F is identified by the

combination of two or more attributes, identifier (F) denotes their


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concatenation. It is worth adding some further notes: It is useful to

emphasize on the fact schema the existence of optional relationships

between attributes in a hierarchy. Optional relationships or optional

attributes of the E/R schema should be marked by a dash; A one-to-one

relationship can be thought of as a particular kind of many-to-one

relationship, hence, it can be inserted into the attribute tree;

Generalization hierarchies in the E/R schema are equivalent to one-to-one

relationships between the super-entity and each sub-entity; x-to-many

relationships cannot be inserted into the attribute tree. In fact,

representing these relationships at the logical level, for instance by a star

schema, would be impossible without violating the first normal form; an

n-ary relationship is equivalent to n binary relationships. Most n-aryrelationships have maximum multiplicity greater than 1 on all their

branches; they determine n one-to-many binary relationships which

cannot be inserted into the attribute tree.)

o Pruning and grafting the attribute tree (Not all of the attributes

represented in the attribute tree are interesting for the DW. Thus, the

attribute tree may be pruned and grafted in order to eliminate the

unnecessary levels of detail. Pruning is carried out by dropping any sub-

tree from the tree. The attributes dropped will not be included in the fact

schema, hence, it will be impossible to use them to aggregate data.

Grafting is used when its descendants must be preserved.).

o Defining dimensions (The dimensions must be chosen in the attribute tree

among the children vertices of the root. E/R schemas can be classified as

snapshot and temporal. A snapshot schema describes the current state of

the application domain; old versions of data varying over time are

continuously replaced by new versions. A temporal schema describes the

evolution of the application domain over a range of time; old versions of

data are explicitly represented and stored. When designing a DW from a

temporal schema, time is explicitly represented as an E/R attribute and

thus it is an obvious candidate to define a dimension. Time is not


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explicitly represented however, should be added as a dimension to the

fact schema).

o Defining fact attributes (Fact attributes are typically either counts of the

number of instances of F, or the sum/average/maximum/minimum of

expressions involving numerical attributes of the attribute tree. A fact

may have no attributes, if the only information to be recorded is the

occurrence of the fact.).

o Defining hierarchies (Along each hierarchy, attributes must be arranged

into a tree such that an x-to-one relationship holds between each node and

its descendants. It is still possible to prune and graft the tree in order to

eliminate irrelevant details. It is also possible to add new levels of

aggregation by defining ranges for numerical attributes. During thisphase, the attributes which should not be used for aggregation but only

for informative purposes may be identified as non-dimension attributes.).

4.5.2.Multidimensional E/R Model

It is argued that ER approach is not suited for multidimensional conceptual

modeling because the semantics of the main characteristics of the model cannot be

effectively represented.

Multidimensional E/R (ME/R) model includes some key considerations [14]:

Specialization of the ER Model

Minimal extension of the ER Model; this model should be easy to learn and use

for an experienced ER Modeler. There are few additional elements.

Representation of the multidimensional aspects; despite the minimality, the

specialization should be powerful enough to express the basic multidimensional

aspects, namely the qualifying and quantifying data and the hierarchical structure

of the qualifying data.

This model allows the generalization concepts. There are some specializations:

A special entity set: dimension level

Two special relationship sets connecting dimension levels:

o a special n-ary relationship set: the fact relationship set


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By modeling the multidimensional cube as a relationship set it is possible to

include an arbitrary number of facts in the schema thus representing a multi-

cube model. Remarkably the schema also contains information about the

granularity level on which the dimensions are shared.

Concerning measures and their structure, the ME/R model allows record

structured measures as multiple attributes for one fact relationship set. The

semantic information that some of the measures are derived cannot be included

in the model. Like the E/R model the ME/R model captures the static structure of

the application domain. The calculation of measures is functional information

and should not be included in the static model. An orthogonal functional model

should capture these dependencies.

Schema contains rolls-up relationship between entities. Therefore levels of

different dimensions may roll up to a common parent level. This information can

be used to avoid redundancies.

This model is used is a relationship.

ME/R and ER models notations can be used together.

Figure 4.14 shows multiple cubes that share dimensions on different levels.

Figure 4.14 Multiple cubes sharing dimensions on different levels

As mentioned above, the ME/R and ER model notations can be used together asillustrated in Figure 4.15.


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Figur e 4.15 Combining ME/R notations with E/R

4.5.3.starER

This model combines star structure with constructs of ER model [13]. The starER

contains facts, entities, relationships and attributes. This model has the following

constructs:

Fact set: represents a set of real world facts sharing the same characteristics or

properties. It is always associated with time. It is represented as a circle.

Entity set: represents a set of real world objects with similar properties. It is

represented as a rectangle. Relationship set: represents a set of associations among entity sets or among

entity sets and fact sets. Its cardinality can be many-to-many, many-to-one, one-

to-many. It is represented as a diamond. Relationship sets among entity sets can

be type of specialization/generalization, aggregation and membership. Figure

4.16 shows the notation for relationship set types.

Figur e 4.16 Notation used in starER


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Attribute: static properties of entity sets, relationship sets, fact sets. It is

represented as an oval.

Fact properties can be of type stock (S) (the state of something at a specific point

in time), flow (F) (the commutative effect over a period of time for some

parameter in the DW environment and which is always summarized) or value-

per-unit (V) (measured for a fixed-time and the resulted measures are not

summarized).

The following criteria are satisfied by the starER schema;

Explicit hierarchies in dimensions

Symmetric treatment of dimensions and summary attributes (properties)

Multiple hierarchies in each dimension

Support for correct summary or aggregation

Support of non-strict hierarchies

Support of many-to-many relationships between facts and dimensions

Handling different levels of granularity at summary properties

Handling uncertainty

Handling change and time

There following list shows the main differences between DF Schema and starER model;

Relationships between dimensions and facts in starER arent only many-to-one,

but also many-to-many, which allows for better understanding of the involved

information.

Object participating in the data warehouse, but not in the form of a dimension are

allowed in the starER.

Specialized relationships on dimensions are permitted

(specialization/generalization, aggregation, membership) and represent more

information. DF requires only a rather straight forward transformation to fact and dimension

tables. This is an advantage of DF Schema. But this is not a drawback for the

starER model, since well-known rules of how to transform an ER Schema

(Which is the basic structural difference between the two approaches) to relations

do exist.


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(base class). An association of classes specifies the relationships between two levels of a

classification hierarchy. These classes must define DAG (Directed Acyclic Graph)

rooted in the dimension class. The DAG structure can represent both alternative path and

multiple classification hierarchies. Descriptor attribute ({D}) define in every class that

represents a classification hierarchy level. Strictness means that an object at a

hierarchys lower level belongs to only one higher level object. Completeness means

that all members belong to one higher-class object and that object consists of those

members only. OOMD approach uses a generalization-specialization relationship to

categorize entities that contain subtypes.

Cube classes represent initial user requirements as the starting point for subsequent data-

analysis phase. Cube classes contain;

Head area; contains the cube classs name.

Measures area; contains the measures to be analyzed.

Slice area; contains the constraints to be satisfied.

Dice area; contains the dimensions and their grouping conditions to address the

analysis.

Cube operations; cover the OLAP operations for a further data analysis phase.

4.6.

Logical Design ModelsDW logical design involves the definition of structures that enable an efficient

access to information. The designer builds multidimensional structures considering the

conceptual schema representing the information requirements, the source databases, and

non functional (mainly performance) requirements. This phase also includes

specifications for data extraction tools, data loading processes, and warehouse access

methods. At the end of logical design phase, a working prototype should be created for

the end-user.

Dimensional models represent data with a cube structure, making more

compatible logical data representation with OLAP data management. The objectives of

dimensional modeling are [10]:


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To produce database structures that are easy for end-users to understand and

write queries against,

To maximize the efficiency of queries.

It achieves these objectives by minimizing the number of tables and relationships

between them. Normalized databases have some characteristics that are appropriate for

OLTP systems, but not for DWs [7]:

Its structure is not easy for end-users to understand and use. In OLTP systems

this is not a problem because, usually end-users interact with the database

through a layer of software.

Data redundancy is minimized. This maximizes efficiency of updates, but tends

to penalize retrievals. Data redundancy is not a problem in DWs because data is

not updated on-line.

Dimensionality modeling uses the ER Modeling with some important restrictions.

Dimensional model composed of one table with a composite primary key, called fact

table, and a set of smaller tables called dimension tables. Each dimension table has a

simple (non-composite) primary key that corresponds exactly to one of the components

of the composite key in the fact table. This characteristic structure is called star schema

or star join.

Another important feature, all natural keys are replaced with surrogate keys. This

means that every join between fact and dimension tables is based on surrogate keys, not

natural keys. Each surrogate key should have a generalized structure based on simple

integers. The use of surrogate keys allows the data in the DW to have some

independence from the data used and produced by the OLTP systems.

4.6.1.

Dimensional Model Design

This section describes a method for developing a dimensional model from an EntityRelationship model [12].

This data model is used by OLTP systems. It contains no redundancy, but high

efficiency of updates, shows all data and relationships between them. Simple queries

require multiple table joins and complex subqueries. It is suitable for technical specialist.


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Classify Entities: For producing a dimensional model from ER model, first

classify the entities into three categories.

o Transaction Entities: These entities are the most important entities in a

DW. They have highest precedence. They construct fact tables in star

schema. These entities record details about particular events (orders,

payments, etc.) that decision makers want to understand and analyze.

There are some characteristics;

It describes an event that occurs at a point in time.

It contains measurements or quantities that may be summarized

(sales amount, volumes)

o Component Entities: These entities are directly related with a transaction

entity with a one-to-many relationship. They have lowest precedence.They define the details or components of each transaction. They answer

the who, what, when, where, how and why of event

(customer, product, period, etc.). Time is an important component of any

transaction. They construct dimension tables in star schema.

o Classification Entities : These entities are related with component entities

by a chain of one-to-many relationship. They are functionally dependent

on a component entity. These entities represent hierarchies embedded in

the data model, which may be collapsed in to component entity to form

dimension tables in star schema.

Identify Hierarchies: Most dimension tables in star schema include embedded

hierarchies. A hierarchy is called maximal if it cannot be extended upwards or

downwards by including another entity. An entity is called minimal if it has no

one-to-many relationship. An entity is called maximal if it has no many-to-one

relationship.

Produce Dimensional Models: There are two operators to produce dimensional

models from ER.

o Collapse Hierarchy: Higher level entities can be collapsed into lower

level entities within hierarchies. Collapsing a hierarchy is a form of

denormalization. This increases redundancy in the form of a transitive


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dependency, which is a violation to 3NF. We can continue doing this

until we reach the bottom of the hierarchy and end up with a single table.

o Aggregation: This operator can be applied to a transaction entity to create

a new entity containing summarized data.

There are 8 models used in dimensional modeling [6, 12]:

Flat Schema

Terraced Schema

Star Schema

Fact Constellation Schema

Galaxy Schema

Snowflake Schema

Star Cluster Schema

Starflake Schema

4.6.2.Flat Schema

This schema is the simplest schema. This is formed by collapsing all entities in the

data model down into the minimal entities. This minimizes the number of tables in the

database and joins in the queries. We end up with one table for each minimal entity in

the original data model [12].

This structure does not lose information from the original data model. It contains

redundancy, in the form of transitive and partial dependencies, but does not involve any

aggregation. It contains some problems; first it may lead to aggregation errors when

there are hierarchical relationships between transaction entities. When we collapse

numerical amounts from higher level transaction entities in to other they will be

repeated. Second this schema contains large number of attributes.

Therefore while the number of tables (system complexity) is minimized, thecomplexity of each table (element complexity) is increased. Figure 4.18 shows a sample

flat schema.


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Figur e 4.18 Flat Schema

4.6.3.Terraced Schema

This schema is formed by collapsing entities down maximal hierarchies, end withwhen they reach a transaction entity. This results in a single table for each transaction

entity in the data model. It causes some problems for inexperienced user, because the

separation between levels of transaction entities is explicitly shown [12]. The Figure

4.19 illustrates a sample terraced schema.


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Figur e 4.19 Terr aced Schema

4.6.4.

Star Schema

It is the basic structure for a dimensional model. It has one fact table and a set of

smaller dimension tables arranged around the fact table. The fact data will not change

over time. The most useful fact tables are numeric and additive because data warehouse

applications almost never access a single record. They access hundreds, thousands,

millions of records at a time and aggregate them. The fact table is linked to all the

dimension tables by one to many relationships. It contains measurements which may be

aggregated in various ways [10, 12, 39].

Dimension tables contain descriptive textual information. Dimension attributes are

used as the constraints in the data warehouse queries. Dimension tables provide the basisfor aggregating the measurements in the fact table. They generally consist of embedded

hierarchies.

Each star schema is formed in the following way;


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A fact table is formed for each transaction entity. The key of the table is the

combination of the keys of its associated component entities.

A dimension table is formed for each component entity, by collapsing

hierarchically related classification entities into it.

Where hierarchical relationships exist between transaction entities, the child

entity inherits all dimensions (and key attributes) from the parent entity. This

provides the ability to drill down between transaction levels.

Numerical attributes within transaction entities should be aggregated by key

attributes (dimensions). The aggregation attributes and functions used depend on

the application.

Star schemas can be used to speed up query performance by denormalizing

reference information into a single dimension table. Denormalization is appropriate

when there are a number of entities related to the dimension table that are often

accessed, avoiding the overhead of having to join additional tables to access those

attributes. Denormalization is not appropriate where the additional data is not accessed

very often, because the overhead of scanning the expanded dimension table may not be

offset by gain in the query performance.

The advantage of using this schema; it reduces the number of tables in the database

and the number of relationships between them and also the number of joins required in

user queries. The Figure 4.20 shows a sample star schema.

Figur e 4.20 Star Schema


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4.6.5.Fact Constellation Schema

A fact constellation schema consists of a set of star schemas with hierarchically

linked fact tables. The links between the various fact tables provide the ability to drill

down between levels of detail[10, 12]. The following figure, Figure 4.21, illustrates a

sample of a fact constellation schema.

Figure 4.21 Fact Constellation Schema

4.6.6.Galaxy Schema

Galaxy schema is a schema where multiple fact tables share dimension tables.

Unlike a fact constellation schema, the fact tables in a galaxy do not need to be directly

related [12]. The following figure, Figure 4.22, illustrates a sample of a galaxy schema.


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Figure 4.22 Galaxy Schema

4.6.7.

Snowflake SchemaIn a star schema, hierarchies in the original data model are collapsed or

denormalized to form dimension tables. Each dimension table may contain multiple

independent hierarchies. A snowflake schema is a variant of star schema with all

hierarchies explicitly shown and dimension tables do not contain denormalized data [10,

12].

The many-to-one relationships among sets of attributes of a dimension can separate

new dimension tables, forming a hierarchy. The decomposed snowflake structurevisualizes the hierarchical structure of dimensions very well.

A snowflake schema can be produced by the following procedure:


combination of the keys of the associated component entities.


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Each component entity becomes a dimension table.


entity inherits all relationships to component entities (and key attributes) from

the parent entity.

Numerical attributes within transaction entities should be aggregated by the key

attributes. The attributes and functions used depend on the application.

The following figure, Figure 4.23, illustrates a sample of a snowflake schema.

Figure 4.23 Snowflake Schema

4.6.8.Star Cluster Schema

While snowflake contains fully expanded hierarchies, which adds complexity to the

schema and requires extra joins, star schema contains fully collapsed hierarchies, which

leads to redundancy. So, the best solution may be a balance between these two schemas

[12]. Overlapping dimensions can be identified as forks in hierarchies. A fork occurs

when an entity acts as a parent in two different dimensional hierarchies. Fork entities can

be identified as classification entities with multiple one-to-many relationships. In Figure

4.24, Region is parent of both Location and Customer entities and the fork occurs at the

Region entity.


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Figur e 4.24 StarSchema with fork

A star cluster schema is a star schema which is selectively snowflaked to separate

out hierarchical segments or sub dimensions which are shared between different

dimensions.

A star cluster schema has the minimal number of tables while avoiding overlapbetween dimensions.

A star cluster schema can be produced by the following procedure:


combination of the keys of the associated component entities.

Classification entities should be collapsed down their hierarchies until they reach

either a fork entity or a component entity. If a fork is reached, a sub dimension

table should be formed. The sub dimension table will consist of the fork entityplus all its ancestors. Collapsing should begin again after the fork entity. When a

component entity is reached, a dimension table should be formed.


entity should inherit all dimensions (and key attributes) from the parent entity.

a comparison of data warehouse design models

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