A Comparison of Data Warehouse Design Models

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A COMPARISON OF DATA WARE HOUSE DESIGN MODELS
A MASTER’S THESIS
in
Computer Engineer ing
Atilim Univer sity
by
BERIL PINAR BAŞARAN
J ANUARY 2005
A COMPARISON OF DATA WARE HOUSE DESIGN MODELS
A THESIS SUBMITTED TO
THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
OF
ATILIM UNIVERSITY
BY
BERIL PINAR BAŞARAN
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF
MASTER OF SCIENCE
IN
THE DEPARTMENT OF COMPUTER ENGINEERING
J ANUARY 2005
i
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. Çağıltay
_____________________
Dr. Ali Arifoğlu
_____________________
Asst. Prof. Dr. Çiğdem Turhan
_____________________
ii
ABSTRACT
A COMPARISON OF DATA WARE HOUSE DESIGN MODELS
Başaran, Beril Pınar
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
iii
ÖZ
VERİ AMBARI TASARIM MODELLERİ KARŞILAŞTIRMASI
Başaran, Beril Pınar
Yüksek Lisans, Bilgisayar Mühendisliği Bölümü
Tez Yöneticisi: Dr. Deepti Mishra
Ortak Tez Yöneticisi: Prof. Dr. Ali Yazici
Ocak 2005, 90 sayfa
Veri ambarı tasarımının kavramsal ve mantıksal tasarım aşamaları için birden fazla
yaklaşım vardır. Kavramsal tasarım safhası için genel olarak kabul görmüş yaklaşımlar
“dimensional
fact”,
“multidimensional
E/R”,
“starER”
ve
“object-oriented
multidimensional” modelleridir. Mantıksal tasarım safhası için genel olarak kabul
görmüş yaklaşımlar “flat”, “terraced”, “star”, “fact constellation”, “galaxy” ,
“snowflake”, “star cluster” ve “starflake” şemalarıdır. Bu tez, kavramsal ve mantıksal
tasarım modellerini karşılaştırır, örnek bir veri ambarı tasarımını ve uygulamasını içerir.
Bu tezde, kavramsal tasarım aşamasında “object-oriented multidimensional” modelinin;
mantıksal tasarım aşamasında performans kriteri açısından “star” şemanın, veri tekrarı
kriteri açısından “snowflake” şemanın en iyi çözümler olduğu gözlendi.
Anahtar Kelimeler: Veri Ambarı, Tasarım Yöntemleri, DF, starER, ME/R, OOMD, flat
şema, terraced şema, star şema, fact constellation şema, galaxy şema, snowflake şema,
star cluster şema, starflake şema, DTS, Data Analyzer
iv
To my dear husband
Thanks for his endless support
v
ACK NO WLEDGEMENTS
First, I would like to thank my thesis advisor Dr. Deepti MISHRA and cosupervisor 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. ÇAĞILTAY, Dr. Ali ARIFOĞLU, Asst. Prof. Dr. Çiğdem
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.
vi
TABLE OF CONTENTS
ABSTRACT ..........................................................................................................................iii
ÖZ........................................................................................................................................... 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.......................................................................... 16
4.1. 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
vii
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........................................................................................................ 41
4.6.5. 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
viii
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
ix
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 ......................................................... 58
5.2 Comparison of conceptual design models .................................................................... 60
5.3 Comparison of logical design models........................................................................... 61
x
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 Extraction............................................................................................................... 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.............................................................................................. 22
4.9 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
xi
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......................................................... 67
6.4 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
xii
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 Relationship
ERD
-
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
xiii
OOMD
-
Object Oriented Multidimensional
RDBMS
-
Relational Database Management Systems
ROLAP
-
Relational OLAP
SQL
-
Structured Query Language
UML
-
Unified Modeling Language
XML
-
Extensible Markup Language
xiv
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 organization’s 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 decisionmakers require access to all the organization’s 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.
1
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 logically
implemented using the logical design models and the physical implementation of a data
warehouse is described.
2
CHAPTER 2
DATA WAREHOUSE CONCEPTS
2.1. Definition of Data War ehouse
A data warehouse (DW) refers to a database that is different from the organization’s
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 end
users 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-or iented, integr ated, time-var iant,
and nonvolatile collection of data in support of management’s decision making process”
[1, 2, 3, 6, 10, 11].
The description of the four key features of the DW is given below.
Subject-or iented: In general, an enterprise contains information that is very detailed to
meet all requirements needed for related subsets of the organization (sales dept, human
resources 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.
3
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 infor mation
Integr ated: 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 Code”
attribute.
Figur e 2.2 Same attr ibute with differ ent for mats in differ ent sour ces
Time-var ian t: DW provides information from a historical prospective. Every key
structure 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.
4
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 OL TP and DW systems
Some of the DW characteristics are given below;

It is a database that is maintained separately from organization’s 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 transactionoriented 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.
5
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
Thousands users, customer-
Hundreds users, market-
Or ientation
oriented, used for
oriented, used for data
transactions and querying
analysis by knowledge
clerks, clients and
workers
Information Technology
(IT) professionals
Data Contents
Manages current data, very
Manages large amounts of
detail-oriented
historical data, provides
facilities for summarization
and aggregation, stores
information at different
levels of granularity to
support decision making
process
Data is continuously
Data is refreshed
updated
Database Design
Data is volatile and
Data is non-volatile and de-
normalized (Entity-
normalized (Dimensional
Relationship (ER) Model)
Model)
Adopts an ER model and an Adopts star, snowflake, or
application-oriented
fact constellation model
database design, index/hash
and a subject-oriented
on primary key.
database design, lots of
scans.
6
View
Focuses on the current data
Spans multiple versions of
within an enterprise or
a database schema due to
department, detailed, flat
the evolutionary process of
relational.
an organization; integrates
information from many
organizational locations and
data stores, summarized,
multidimensional.
Access Patter ns
Short, atomic, simple
Mostly read-only
transactions; requires
operations, complex query,
concurrency control and
although many could be
recovery mechanisms, day
complex queries, long term
to day activities, mostly
informational requirements,
updates.
decision support, mostly
reads.
Table 2.1 Comp ar ison of OLTP and OLAP
It may seem questionable to implement a DW system for companies running their
business on OLTP systems. The following list compares the main reasons for using a
DW;

To gain high performance of both systems by proper data organization (DB
design).

OLTP deals with transactions, concurrency, locking and logging. OLTP deals
with many records at ones. Transaction performance of OLTP and selection
performance of OLAP would be in conflict.

Different structures, contents, and uses of the data. OLTP requires the current
data. OLAP requires historical data.

Data Cleanness. Data in OLTP might be “dirty” because it is collected by clerks
that may make mistakes and for other reasons. Data that goes into OLAP should
be cleaned and standardized.
7

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. Requir ements 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
8
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 per for m ance: Data warehouses need incremental loading of data
periodically so the load process performance should be like gigabytes of data per
hour.

Load pr ocessing: 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 m anagement: 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.

Quer y Per for mance: Complex queries must complete in acceptable periods.

Ter abyte 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.

War ehouse administr ation: Easy-to-use and flexible administrative tools
should exists for data warehouse administration.

Advanced quer y functionality: The data warehouse RDBMS should supply
advanced analytical operations to enable end-users perform advanced
calculations and analysis.
9
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.
Figur e 3.1 Ar chitectur e of DW
10
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 and
semantics 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-LoadTransform (ETL) process (data acquisition) are determined. Finally, decisions about
persistent storage of the warehouse using database technology and the various ways data
will 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 provided
by 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
11
(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 an
essential 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.
12
3.1.1. Extr action, Cleansing and Tr ansfor mation 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 gener ator s
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 don’t 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 ansfor mation engines
Rule-driven dynamic transformation engines capture data from a source system at userdefined 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 now
emerging that handle non-relational source files and databases.
3.2. Data Stor age and Access
Because of the special nature of warehouse data and access, accustomed
mechanisms for data storage, query processing and transaction management must be
13
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 company’s 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 Mar ts
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 a
data 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.
14

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 per for mance: 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 mar ts: A solution to this problem is building
virtual data marts which are views of several physical data marts.
Administr ation: 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.
15
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 Oper ational Data
Creating the DW does not only involve extracting operational data and entering it
into the warehouse (Figure 4.1) .
Figur e 4.1 Data Extr action
16
Pulling the data into the DW without integrating it is a big mistake ( Figure 4.2 ).
Figur e 4.2 Data Integr ation
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, differ ent 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 time
consuming and unrealistic.
Three types of data are loaded into the DW from the operational system:

Archival data

Data currently contained in the operational environment
17

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 data’s 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/Pr ocess Models
The 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
18
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.
19
Figur e 4.4 A Simple ERD for a manufactur ing envir onment
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.
Figur e 4.5 Cor p or ate ERD cr eated b y depar tmental ERDs
20
4.3.2. Mid-Level Modeling
After the high-level data model is created, the next level is established—the
midlevel model or the DIS. For each major subject area, or entity, identified in the highlevel 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
Figur e 4.7 Midlevel model members
21
The primary grouping exists once, and only once, for each major subject area. It
holds attributes that exist only once for each major subject area. As with all groupings of
data, the primary grouping contains attributes and keys for each major subject area.
The secondary grouping holds data attributes that can exist multiple times for each
major subject area. This grouping is indicated by a line drawn downward from the
primary grouping of data. There may be as many secondary groupings as there are
distinct groups of data that can occur multiple times.
The third construct is the connector. The connector relates data from one grouping
to another. A relationship identified at the ERD level results in an acknowledgement at
the DIS level. The convention used to indicate a connector is an underlining of a foreign
key.
The fourth construct in the data model is “type of” data. “Type of” data is indicated
by a line leading to the right of a grouping of data. The grouping of data to the left is the
supertype. The grouping of data to the right is the subtype of data.
These four data modeling constructs are used to identify the attributes of data in a
data model and the relationship among those attributes. When a relationship is identified
at the ERD level, it is manifested by a pair of connector relationships at the DIS level.
A sample model is drawn in Figure 4.8 below.
Figur e 4.8 A Midlevel model sample
Like the corporate ERD that is created from different ERDs reflecting the
community of users, the corporate DIS is created from multiple DISs. Figure 4.9 shows
a sample corporate DIS formed by many departments DISs.
22
Figur e 4.9 Cor p or ate DIS for med by depar tmental DISs.
Figure 4.10 shows an individual department’s 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
23
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.
Figur e 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].
24
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 dimension
is 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.
25
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 precalculations.
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 and
interpreting 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-user’s
perception of the data mart are physical sort order of the fact table 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.
26
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.
27
Figur e 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. A
non-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 sum
operator 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
28
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-ary
relationships 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 subtree 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
29
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 this
phase, 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
30
o a special binary relationship set: the ‘roll-up to’ relationship set
The ‘roll-up to’ relationship set; it relates a dimension level A to a dimension level
B representing concepts of a higher level of abstraction (city roll-up to country).
The ‘fact’ relationship set is a specialization of a general n-ary relationship set. It
connects n different dimension level entities.
The fact relationship set models the natural separation of qualifying and quantifying
data. The attributes of the fact relationship set model the measures of the fact while
dimension levels model the quantifying data. This model uses a special graphical
notation which a sample notation is shown in Figure 4.13 .
Figur e 4.13 The gr aphical notation of ME/R elements
Individual characteristics of ME/R model may be summarized as follows;

A central element in the multidimensional model is the concept of dimensions
that span the multidimensional space. The ME/R model does not contain an
explicit counterpart for this idea. This is not necessary because a dimension
consists of a set of dimension levels. The information which dimension-levels
belong to a given dimension is included implicitly within the structure of the
rolls-up graph.

The hierarchical classification structure of the dimensions is expressed by
dimension level entity sets and the roll-up relationships. The rolls-up relationship
sets define a directed acyclic graph on the dimension levels. This enables the
easy modeling of multiple hierarchies, alternative paths and shared hierarchy
levels for different dimensions. Thus no redundant modeling of the shared levels
is necessary. Dimension level attributes are modeled as attributes of dimension
level entity sets. This allows a different attribute structure for each dimension
level.
31

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 ‘multicube 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.
Figur e 4.14 Multiple cubes shar ing dimensions on differ ent levels
As mentioned above, the ME/R and ER model notations can be used together as
illustrated in Figure 4.15.
32
Figur e 4.15 Combining ME/R notations with E/R
4.5.3. star ER
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, oneto-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 star ER
33

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 valueper-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 aren’t 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.
34

starER model combines the powerful constructs of the ER model with the star
schema.
A sample DW model using starER is illustrated in Figure 4.17.
Figur e 4.17 A sample DW model using star ER
4.5.4. Ob ject-Or iented Multidimensional Model (OOMD)
Unified Modeling Language (UML) has been widely accepted as a standard objectoriented modeling language for software design. OOMD modeling approach is based on
UML. In OOMD model, dimensions and facts are represented by dimension classes and
fact classes [18, 19]. Fact classes are considered as composite classes in a sharedaggregation relationship of n-dimension classes. In this way, many-to-many
relationships between facts and particular dimensions are represented by indicating 1..*
cardinality on the dimension class. Fact classes cardinality is defined as * to indicate that
a dimension object can be part of one, zero or more fact object instances. The minimum
cardinality of dimension classes is defined as 1 to indicate that a fact object is always
related to object instances from all dimensions. Derived measures are placed in fact class
by notation after “/”. Derivation rules appear between braces. Identifying attribute can
be defined in fact classes by notation after {OID} (Object Identifier). All measures are
additive (Sum operator can be applied to aggregate measure values along all
dimensions). For dimensions, every classification hierarchy level is specified by a class
35
(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
hierarchy’s 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 dataanalysis phase. Cube classes contain;

Head area; contains the cube class’s 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 Models
DW 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]:
36

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 Entity
Relationship 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.
37

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
38
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, the
complexity of each table (element complexity) is increased. Figure 4.18 shows a sample
flat schema.
39
Figur e 4.18 Flat Schema
4.6.3. Ter r aced Schema
This schema is formed by collapsing entities down maximal hierarchies, end with
when 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.
40
Figur e 4.19 Ter r aced Schem a
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 basis
for aggregating the measurements in the fact table. They generally consist of embedded
hierarchies.
Each star schema is formed in the following way;
41

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
42
4.6.5. Fact Constellation Schem a
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.
Figur e 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.
43
Figur e 4.22 Galaxy Schema
4.6.7. Snowflake Schem a
In 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 structure
visualizes the hierarchical structure of dimensions very well.
A snowflake schema can be produced by the following procedure:

A fact table is formed for each transaction entity. The key of the table is the
combination of the keys of the associated component entities.
44

Each component entity becomes a dimension table.

Where hierarchical relationships exist between transaction entities, the child
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.
Figur e 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.
45
Figur e 4.24 Star Schema 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 overlap
between dimensions.
A star cluster schema can be produced by the following procedure:

A fact table is formed for each transaction entity. The key of the table is the
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 entity
plus all its ancestors. Collapsing should begin again after the fork entity. When a
component entity is reached, a dimension table should be formed.

Where hierarchical relationships exist between transaction entities, the child
entity should inherit all dimensions (and key attributes) from the parent entity.

Numerical attributes within transaction entities should be aggregated by the key
attributes (dimensions). The attributes and functions used depend on the
application.
The Figure 4.25 illustrates a sample diagram of star cluster schema.
46
Figur e 4.25 Star Cluster Schema
4.6.9. Star flake Schema
Starflake schema is a hybrid structure that contains a mixture of star and snowflake
schemas. The most appropriate database schemas use a mixture of denormalized star and
normalized snowflake schemas [6, 41]. The Figure 4.26 illustrates a sample diagram of
starflake schema.
Figur e 4.26 Star flake Schem a
47
Whether the schema star, snowflake or starflake, the predictable and standard form
of the underlying dimensional model offers important advantages within a DW
environment including;

Efficiency; the consistency of the database structure allows more efficient access
to the data by various tools including report writers and query tools.

Ability to handle changing requirements; The star schema can adapt to changes
in the user requirements, as all dimensions are equivalent in terms of providing
access to the fact table.

Extensibility; the dimensional model is extensible. It must support adding new
dimensions, adding new dimensional attributes, breaking existing dimension
records down to lower level of granularity from a certain point in time forward.

Ability to model common business situations

Predictable query processing
The following figure, Figure 4.27, shows a comparison of the logical design methods in
complexity versus redundancy trade-off.
Figur e 4.27 Compar ison of schemas
4.6.10. Cube
Cubes are the logical storage structures for OLAP databases. A cube defines a set of
related dimensions; each cell of the cube hold one value, the value of each cell is an
48
intersection of the dimensions. A 2-dimensional view of an OLAP table is given in
Table 4.1.
Table 4.1 2-dimensional pivot view of an OLAP Table
A 3-dimensional view of an OLAP table is given in Table 4.2.
Table 4.2 3-dimensional pivot view of an OLAP Table
A 3-D realization of the cube shown in Table 4.2 is illustrated in Figure 4.28.
49
Figur e 4.28 3-D Realization of a Cube
The cube has three dimensions which are time, state and product. Each dimension
enables you to perform specific OLAP operations on the cube. The basic OLAP
operations are as follows [10, 23, 24]:

Roll up: An operation for moving up the hierarchy level and grouping into larger
units along a dimension. Using roll up capability, users can zoom out to see a
summarized level of data. Roll up operation is also called the drill up operation.

Drill down: An operation for moving down the hierarchy level and stepping
down the hierarchy. Using drill down capability, users can navigate to higher
levels of detail. Drill down operation is the reverse of roll up operation.

Slice: Slicing performs a selection on one dimension of a cube and results in a
sub cube. Slicing cuts through the cube so that users can focus on more specific
perspectives.

Dice: Slicing defines a sub cube by performing a selection on two or more
dimensions of a cube.
50

Pivot: Pivot operation is also called rotate operation. Pivoting is a visualization
operation which rotates the data axes in view in order to provide an alternative
presentation of the data.
A detailed figure describing the operations above is illustrated below in Figure 4.29.
51
Figur e 4.29 Oper ations on a Cube
52
4.7. Meta Data
An important component of the DW environment is meta data. Meta data, or data
about data, provides the most effective use of the DW. Meta data allows the end
user/DSS analyst to navigate through the possibilities. In other words, when a user
approaches a data warehouse where there is no meta data, the user does not know where
to begin the analysis.
Meta data acts like an index to the data warehouse contents. It sits above the
warehouse and keeps track of what is where in the warehouse. Typically, items the meta
data store tracks are as follows [6]:

Structure of data as known to the programmer and to the DSS analyst

Source data

Transformation of data

Data model

DW

History of extracts
Metadata has several functions within the DW that relates to the processes
associated with data transformation and loading, DW management and query generation.
The metadata associated with data transformation and loading must describe the source
data and any changes that were made to the data. The metadata associated with data
management describes the data as it is stored in the DW. Every object in the database
needs to be described including the data in each table, index and view and any
associated constraints. The metadata is also required by the query manager to generate
appropriate queries.
4.8. Mater ialized views
They address the problem of selecting a set of views to materialize in a DW taking
into account [7]:

the space allocated for materialization

the ability of answering a set of queries (defined against the source relations)
using exclusively these views
53

the combined query evaluation and view maintenance cost
In this proposal they define a graph based on states and state transitions. They
define a state as a set of views plus a set of queries, containing an associated cost.
Transitions are generated when views or queries are changed. They demonstrate that
there is always a path from an initial state to the minimal cost state.
4.9. OLAP Ser ver Ar chitectur es
Logically, OLAP engines present business users with multidimensional data from
data warehouses or data marts, without concerns regarding how or where the data are
stored. However, the physical architecture and implementation of OLAP engines must
consider data storage issues. Implementations of a warehouse server engine for OLAP
processing include [10]:
Relation al OLAP (ROLAP) servers: These are the intermediate servers that stand in
between a relational back-end server and client front-end tools. They use a relational or
extended-RDBMS to store and manage warehouse data, and OLAP middleware to
support missing pieces. ROLAP servers include optimization for each DBMS back-end,
implementation of aggregation navigation logic, and additional tools and services.
ROLAP technology tends to have greater scalability than MOLAP technology.
Multidimensional OLAP (MOLAP) servers: These servers support multidimensional
views of data through array-based multidimensional storage engines. They map
multidimensional views directly to data cube array structures. The advantage of using a
data cube is that it allows fast indexing to precomputed summarized data. Notice that
with multidimensional data stores, the storage utilization may be low if the data set is
sparse. In such cases, sparse matrix compression techniques should be explored. Many
OLAP servers adopt a two-level storage representation to handle sparse and dense data
sets: the dense subcubes are identified and stored as array structures, while the sparse
subcubes employ compression technology for efficient storage utilization.
Hybr id OLAP (HOLAP) ser vers: The hybrid OLAP approach combines ROLAP and
MOLAP technology, benefiting from the greater scalability of ROLAP and the faster
computation of MOLAP. For example, a HOLAP server may allow large volumes of
54
detail data to be stored in a relational database, while aggregations are kept in a separate
MOLAP store.
55
CHAPTER 5
COMPARISON OF MULTIDIMENSIONAL DESIGN MODELS
5.1. Compar ison of Dimensional Models and ER Models
The main objective of ER modeling is to remove redundancy from data. ER
modeling aims to optimize performance for transaction processing.
To remove redundancy, designers must use hundreds of entities and relations
between entities, which makes ER model complex. There is no easy way to enable endusers navigate through the data in ER models. It is also hard to query ER models
because of the complexity; many tables should be joined to obtain a result set. Therefore
ER models are not suitable for high performance retrieval of data.
The dimensional model is a standard framework. End user tools can make strong
assumptions about the dimensional model to make user interfaces more user friendly and
to make processing more efficient [20].
Dimensional model is more adaptable to unexpected changes in user behavior and
requirements. The logical design can be made independent of expected query patterns.
All dimensions can be thought as symmetrically equal entry points into the fact table.
Dimensional model is extensible to new design decisions and data elements. All
existing fact and dimension tables can be changed in place without having to reload data.
End user query and reporting tools are not affected by the change.
56
ER modeling does not involve business rules, it involves data rules. Dimensional
model involves business rules.
5.2. Compar ison of Dimensional Models and Ob ject-Or iented Models
Dimensional data modeling (DDM) is a dimensional model design methodology
that for each business process, it enumerates relevant measures and dimensions. A DDM
provides a multidimensional conceptual view of data. Although DDM is the favorite
approach in data warehousing, it focuses mainly on data and its proper structuring to
maximize query performance. DDM approach lacks modeling business goals and
processes. Although the final logical and physical model will be a dimensional data
model, object-oriented (OO) model is much stronger in the logical and conceptual
design phases. We should consider DDM approach and OO approach as complementary
to each other, a logical design modeled by an OO model can be mapped easily to a
DDM model.
Various approaches have been developed for the conceptual design of
multidimensional systems in the last years to represent multidimensional structural and
dynamic properties. However, none of them has been accepted as a standard for
multidimensional modeling. On the other hand, UML has been accepted as the standard
OO modeling language for describing and designing various aspects of software
systems. Using UML,
OO model allows modeling of the business process, sub
processes, use cases, system actors, classes, objects, collaboration of objects, relations
between object and finally components, which are basically reusable software packages.
Objects have types, properties (attributes), behaviors, methods and relations with other
objects like aggregation, inheritance and association.
A DDM approach is basically an approach in which tables are associated with SQL
methods to support set-oriented processing of data and return result set to the caller. On
the other hand, OO approach provides an object layer to a DW application unifying
behavior and data within the object components.
An OO approach provides a tighter conceptual association between strategic
business goals and objectives and the DDM model. It starts with the specification of
business goals, then modeling of the processes and use cases. Based on the use cases,
57
objects are modeled. The resulting DDM model will be valid with the corresponding
business requirements. In addition to the discussion above, I summarized the comparison
of ER, DM and OO methodologies according to factors, I consider as the most
important, in Table 5.1.
ER
DM
OO
Standard notation


 (UML)
Business rules focus



Data rules focus



Ability to model all business



Specialization / Generalization



Commercial case tool availability



High association with business






requirements in detail
objectives
Adaptability to changing
requirements
Table 5.1 Comp ar ison of ER, DM and OO meth odologies
5.3. Compar ison of Conceptual Multidimensional Models
In sections 5.1 and 5.2, the main conceptual modeling approaches are mentioned.
This section gives a comparison of conceptual multidimensional models according
multidimensional modeling properties [13, 14, 15, 16, 17, 18, 19].

Additivity of measures: DF, starER and OOMD support this property. Using
ME/R model, only static data structure can be captured. No functional aspect can
be implemented with ME/R model; therefore ME/R does not support this
property.

Many-to-many relationships with dimensions: StarER and OOMD support this
property. DF and ME/R models do not support many-to-many relationships.

Derived measures: None of the conceptual models include derived measures as
part of their conceptual schema except OOMD model.
58

Nonstrict and complete classification hierarchies: Although DF, starER and
ME/R can define certain attributes for classification hierarchies, only starER
model can define exact cardinality for nonstrict and complete classification
hierarchies. OOMD can represent nonstrict and complete classification
hierarchies.

Categorization of dimensions (specialization/generalization): DF does not
support this property. Since starER and ME/R models derive from ER model,
these two models use is-a relationship to categorize dimensions. Since OOMD
model is object-oriented, it has support for dimension categorization. Note that
specialization/generalization is a basic aspect of object-orientation.

Graphic notation and specifying user requirements: All modeling techniques
provide a graphical notation to help designers in conceptual modeling phase.
Only ME/R model provides state diagrams to model system’s behavior and
provides a basic set of OLAP operations to be applied from these user
requirements. OOMD provide complete set of UML diagrams to specify user
requirements and help define OLAP functions.

Case tool support: All conceptual design models except starER have case tool
support. Conceptual design using DF approach can be implemented using the
WAND case tool [31]. Conceptual design using ME/R approach can be
implemented using GramMi case tool [29]. With the OOMD approach, the
conceptual design may be implemented using Microsoft® Visio, Rational Rose
or GOLD case tools [18, 30].
The Table 5.2 summarizes the comparison given above.
starER
DF
ME/R
OOMD




relationships 







complete 






Additivity of measures
Many-to-many
with dimensions
Derived measures
Nonstrict
and
classification hierarchies
Categorization of dimensions 
59
(specialization/generalization)
Graphic notation




Specifying user requirements




Case tool support
WAND GRAMMI GOLD, Rational Rose,
MS Visio
Table 5.2 Compar ison of conceptu al design models
5.4. Compar ison of L ogical Design Models
Among the logical design models, star schema, snowflake schema and the fact
constellation schema are the mostly used models commercially. In this section, I want to
compare these three models in terms of efficiency, usability, reusability and flexibility
quality factors. I think efficiency is the most important factor in DW modeling. A DW is
usually a very large database. Because many queries will access large amounts of data
and involve multiple join operations, efficiency becomes a major consideration [22].
A star schema is generally the most efficient design for two reasons. First, a design
with denormalized tables need fewer joins. Second, most optimizers recognize star
schemas and can generate efficient “star join” operations. A fact constellation schema is
a set of star schemas with hierarchically. A fact constellation schema may need more
join operations on fact tables. Similarly, a snowflake schema will require more joins on
dimension tables. In some cases where the denormalized dimension tables in star schema
becomes very large, a snowflake schema may be the most efficient design approach.
In terms of usability, a number of advantages may be considered for star schema
design approach. The star schema is the simplest structure among the three schemas.
Because a star schema has the fewest number of tables, users need to execute fewer join
operations which makes it easier to formulate analytic queries. It is easier to learn star
schema compared to other two schemas. Fact constellation schema and snowflake
schema are more complex than star schema which is a disadvantage in terms of
usability.
Considering reusability, the snowflake schema is more reusable than star and fact
constellation schemas. Dimension tables in a snowflake schema do not contain
60
denormalized data. This makes it easier to share dimension tables between snowflake
schemas in a DW. In star schema and fact constellation schema design approaches,
dimension tables are denormalized and this makes it less convenient to share dimension
tables between schemas.
In terms of flexibility, a star schema is more flexible in adapting to changes in user
requirements. The star schema can adapt to changes in the user requirements easier, as
all dimensions are equivalent in terms of providing access to the fact table. Table 5.3
summarizes the comparison of the three logical design models in terms of quality
factors.
Star Schema
Snowflake Schem a
Fact Constellation
Schema
Efficiency
High
Low
Moderate
Usability
High
Low
Moderate
Reusability
Low
High
Moderate
Flexibility
High
Low
Moderate
Table 5.3 Compar ison of logical design models
5.5. Discussion on Data War ehousing Design Tools
There are CASE tools that enable a user to design DW architecture. Some of the
CASE tools are mentioned in section 5.3. Current commercial CASE tools have great
design options to model that enable modelers to model databases and software solutions
even in the enterprise level. These tools can generate code that may be used in the
development phase in forms of VB.NET, C#, C++ and may reverse engineer a given
source code project into software models. Using these CASE tools, a designer can also
generate databases via the tool and reverse engineer databases into model using the
design model diagrams. Unfortunately, the development of the CASE tools on the data
warehousing area is not as mature as the development in ER and software modeling
areas. Very few commercially available tools may help in designing data warehousing
solutions and may still not cover the requirements you need.
61
A CASE tool without basing the modeling notation on UML may never cover the
needs of data warehousing design. Note that, the main purpose of UML is to “represent
business rules” [26].
Unfortunately, a case tool for a complete data warehousing design is not available.
But there are still solutions using the existing CASE tools in data warehousing arena. As
Kimball mentions [27] “ I am cautious about recommending methodologies and CASE
tools from traditional environments for data warehouse software development.
Methodologies scare me the most. So often I see a methodology becoming the goal of a
project, rather than just being a tool. Recently someone asked me: “What is the
difference between a methodologist and a terrorist?” I answered, “At least you can
negotiate with a terrorist.” “. Finally, a designer/developer should use the existing CASE
tools for the development of a DW.
There are some factors in which data warehouse software developers must consider
to make more use of existing software development tools. These topics are summarized
as follows:

Definition and declaration of business rules: Communication and documentation
are very important for data warehouse business rules design. Unfortunately, we
should not expect significant automatic conversion of the most complex business
rules into code using the present commercial CASE tools. Complex business
rules still remain in a documentation-only form, and probably will only be
enforced by a human designer. The best CASE tool meeting this requirement
may be Microsoft® Visio 2003 Enterprise Edition.

Database Design: Most of the commercial CASE tools enable designing ER
databases. It should be addressed that although these CASE tools may support
generating ER databases from models and reverse engineering from an ER
generated database into an ER model, these tools do not provide a complete
solution for an OLAP solution. For designing a database Microsoft® Visio,
ERWin or WarehouseArchitect may be used.

ETL Process: The commercially available tools for ETL process meets the
requirements needed by this process. The widely used ETL product is
62
Microsoft® Data Transformation Services (DTS) which is a built-in feature of
Microsoft® SQL Server 2000. DTS allows defining ETL processes on both its
built-in designer or using the COM API of DTS or using scripting language. This
tool can accept any data source that has an ODBC or OLEDB provider. As an
example, a data source may be Microsoft® SQL Server 7.0, Microsoft® SQL
Server 2000, Oracle, Sybase, DB2, a text file, an Microsoft® Access database,
an Microsoft® Excel spreadsheet (all versions), etc. In this thesis, the ETL
process of the sample DW solution is implemented using DTS and described in
section 6.5.

Metadata Repository Design: Vendors like Microsoft, IBM, Oracle have all
either defined a global metadata repository format or promised the market that
they will do so in the future. But, the world needs a single standard instead of
multiple repository definitions. Most analysts believe that Microsoft’s repository
effort is the one most likely to succeed. “In 1997, Microsoft turned over the
content of its proprietary repository design to the Metadata Coalition, and
worked with a group of vendors to define the Open Information Model (OIM),
intended to support software development and data warehousing” [27]. Microsoft
and many other vendors are actively programming their tools to read and write
from the Microsoft Repository.

Communication: While the nature of data warehousing requires the need of
consolidating data from heterogeneous data sources, data sources may become
homogenous. As XML become a standard of communication, the XML web
services appeared. “An XML Web service is a programmable entity that provides
a particular element of functionality, such as application logic, and is accessible
to any number of potentially disparate systems using ubiquitous Internet
standards, such as XML and HTTP” [28] . Web services change the nature of
DWs.
63
CHAPTER 6
IMPLEMENTING A DATA WAREHOUSE
6.1. A Case Study
This section describes a case study of implementing a DW. A database is designed
for simulating an OLTP application. The aim of this case study is to build a DW to
enable analysis of the data in the OLTP database. The conceptual design is modeled
using OOMD, starER, ME/R and DF models to give example designs for all approaches.
The DW is designed using snowflake schema. And finally this case study illustrates a
implementation of a data warehousing solution covering all phases of design. The ER
model of the OLTP database that simulates the basis for our DW is shown in Figure 6.1.
64
Figur e 6.1 ER model of sales and shipping systems
6.2. OOMD Appr oach
Data warehousing has largely developed with little or no reference to objectoriented software engineering. This is consistent with (a) its development out of two-tier
client/server relational database methodology, and (b) its character as a kind of highlevel systems integration, rather than software development, activity. Data Warehousing
assembles components, rather than creating them. The initial top-down centralized data
warehousing model with its single database server, limited middleware, and GUI frontend, could get by with a pragmatic "systems integration" orientation at the global level
of component interaction, while restricting explicit object-orientation to the various
GUI, middleware, analysis, web-enabling, data transformation, and reporting tools that
comprise the data warehousing arsenal.
The days of two-tier client/server-based data warehouses are gone now. The
dynamic of the data warehousing business and its own development of new technology,
65
has caused centralized data warehouses to be increasingly supplemented with data marts,
with data stores of diverse type and content, with specialized application servers, with
design and metadata repositories, and with internet, intranet, and extranet front-ends.
The two-tier client/server paradigm has given way to a multi-tier reality characterized by
an increasing diversity of objects/components, and by an increasing complexity of object
relations. Moreover this reality is one of physically distributed objects across an
increasingly complex network architecture. Data warehouse development now requires
the creation of a specific distributed object/component solution, and the evolutionary
development of this solution over time, rather than the creation of a classical two-tier
client/server application [25].
I strongly believe it is more convenient to model complex software systems with
OO design approach using UML. So, before going to implementation details, I would
like to introduce some sample diagrams for the sales and shipping system using the OO
conceptual modeling approach.
Respectively, Figure 6.2 illustrates the use case
diagram, Figure 6.3 shows the statechart diagram and Figure 6.4 illustrates the static
structure diagram of sales and shipping system using UML notation.
Figur e 6.2 Use case diagr am of sales and shipping system
66
Use case diagrams are used to describe real-world activities. A use case is a set of
events that occurs when an actor uses a system to complete a process. Normally, a use
case is a relatively large process.
Figur e 6.3 Statechar t diagr am of sales and shipping system
Statechart diagrams are used to show the sequence of states an object goes through
during its life.
Figur e 6.4 Static str uctur e diagr am of sales and shipping system
67
Static structure diagrams are used to create conceptual diagrams that represent
concepts from the real world and the relationships between them, or class diagrams that
decompose a software system into its parts.
UML modeling has also the following diagrams that give modeler great flexibility
and helps understandability during the conceptual and logical design phases. Package
diagrams are used to group related elements in a system. One package can contain
subordinate packages, diagrams, or single elements. Activity diagrams are used to
describe the internal behavior of a method and represent a flow driven by internally
generated actions. Sequence diagrams are used to show the actors or objects
participating in an interaction and the events they generate arranged in a time sequence.
Collaboration diagrams are used to show relationships among object roles such as the set
of messages exchanged among the objects to achieve an operation or result. Component
diagrams are used to partition a system into cohesive components and show the structure
of the code itself. Deployment diagrams are used to show the structure of the run-time
system and communicate how the hardware and software elements that make up an
application will be configured and deployed.
Static structure diagram in Figure 6.4 forms the basis of the MD model. This
diagram can easily be mapped to MD model. “Sales” and “Shipping” classes form the
fact tables, “Employee”, “Shipper”, “Region”, “State”, ”Country”, “Product”,
“Product_SubCategory” and “Product_Category” classes form the dimension tables.
6.3. star ER Appr oach
In this section, the conceptual model is designed using starER approach. Figure 6.5
illustrates the sales subsystem starER model. The following list describes the items in
the starER model.

The items represented as circles are fact sets.

The items represented as rectangles are entity sets.

The items represented as diamonds are relationship sets.

The items respresented as ovals are attributes.
68
The model contains one fact set (sales) and four dimensions (employee, product,
time and state). The time, product and state dimensions contain hierarchies that enable
summarization of the fact set by different granularities. Non-complete membership is
shown in these dimensions.
Figur e 6.5 Sales subsystem star ER model
Figure 6.6 illustrates the shipping subsystem starER model.
69
Figur e 6.6 Shipping subsystem star ER model
The model contains one fact set (shipping) and five dimensions (shipper, product,
time and state_from, state_to). The time, product and states dimensions contain
hierarchies that enable summarization of the fact set by different granularities. Noncomplete membership is shown in these dimensions.
It is relatively easy and straightforward to design the conceptual model using
starER modeling technique having the ER of the OLTP system.
6.4. ME/R Appr oach
In this section, the conceptual model is designed using ME/R approach. Figure 6.7
illustrates the sales subsystem with ME/R approach.
The first design step is determining dimensions and facts. Fact relationship connects
the sales fact with the dimensions state, product, day and employee. The rolls-up
relationships (arrow shapes) are shown in the model below. The fact relationship in the
middle of the diagram connects the atomic dimension levels. Each dimension is
70
represented by a subgroup that starts at the corresponding atomic level. The actual facts
(sales units, sales dollars) are modelled as attributes of the fact relationship. The
dimension hierarchies are shown by the rolls-up relationships (e.g. Product rolls-up to
product_subcategory and product_category). Additional attributes of a dimension level
(e.g. employee_id and employee_name of an employee) are depicted as dimension
attributes of the corresponding level.
Figur e 6.7 Sales subsystem ME/R model
Figure 6.8 illustrates the shipping subsystem with ME/R approach and is similar to the
sales subsytem.
71
Figur e 6.8 Shipping subsystem ME/R model
6.5. DF Appr oach
In this section, the conceptual model is designed using DF approach. Figure 6.9
illustrates the sales subsystem with DF approach. The sales fact scheme is structured as a
tree whose root is the sales fact. The fact is illustrated as a rectangle and contains the
fact name (sales) and measures (sales_dollars, sales_units). Each vertex directly attached
to the fact is a dimension. The dimensions in the sales model are product, time, state and
employee. Subtrees rooted in dimensions are hierarchies. Their vertices, represented by
circles, are attributes (product_ID); their arcs represent -to-one relationships between
pairs of attributes. The vertices in the fact schema represented by lines instead of circles
are non-dimension attributes (product_name).
72
Figur e 6.9 Sales subsystem DF model
Figure 6.10 illustrates the shipping subsystem with DF approach.
Figur e 6.10 Shipping subsystem DF model
73
6.6. Implementation Details
In this section, using the OOMD approach and taking the static structure diagram in
Figure 6.4 as the basis, the physical implementation of the sample DW is given. In the
model, there are two fact tables; “sales” and “shipping”. I have chosen snowflake
schema for the implementation of the DW. The main reason for choosing the snowflake
schema is that, the sample OLTP database I have prepared as the data source of my
sample DW is completely formed of normalized tables and therefore using snowflake
schemas in the design of the DW is more applicable and easier to implement. The
sample DW is implemented using Microsoft® OLAP Server [8].
The snowflake schema for the sales subsystem is illustrated in Figure 6.11.
Figur e 6.11 Snowflake schema for the sales subsystem.
74
The snowflake schema for the shipping subsystem is illustrated in Figure 6.12.
Figur e 6.12 Snowflake schema for the shipping subsystem.
The diagram in Figure 6.13 illustrates the general architecture of the case study.
Figur e 6.13 Gener al ar chitectur e of the case study
The ETL process is implemented using Microsoft® Data Transformation Services
(DTS). DTS is a set of graphical tools and programmable objects that let the designer
extract, transform, and consolidate data from different sources into single or multiple
destinations. A DTS package is an organized collection of connections, DTS tasks, DTS
75
transformations, and workflow constraints assembled either with a DTS tool or
programmatically and saved to Microsoft® SQL Server, Microsoft® SQL Server 2000
Meta Data Services, a structured storage file, or a Microsoft® Visual Basic file. Each
package contains one or more steps that are executed sequentially or in parallel when the
package is run. When executed, the package connects to the correct data sources, copies
data and database objects, transforms data, and notifies other users or processes of
events. Packages can be edited, password protected, scheduled for execution, and
retrieved by version. A DTS transformation is one or more functions or operations
applied against a piece of data before the data arrives at the destination. The source data
is not changed. For example, the user can extract a substring from a column of source
data and copy it to a destination table. The particular substring function is the
transformation mapped onto the source column. The user also can search for rows with
certain characteristics (for example, specific data values in columns) and apply functions
only against the data in those rows. Transformations make it easy to implement complex
data validation, data scrubbing, and conversions during the import and export process.
DTS is based on an OLE DB architecture that allows copying and transforming data
from a variety of data sources. Some of these are listed below:

SQL Server and Oracle directly, using native OLE DB providers.

ODBC sources, using the Microsoft® OLE DB Provider for ODBC.

Microsoft® Access 2000, Microsoft® Excel 2000, Microsoft® Visual FoxPro,
dBase, Paradox, HTML, and additional file data sources.

Text files, using the built-in DTS flat file OLE DB provider.

Microsoft® Exchange Server, Microsoft® Active Directory and other
nonrelational data sources.

Other data sources provided by third-party vendors.
In the sample implementation, the sales fact table and the shipping fact table are
populated from a delimited text file, an Access database, an Excel spreadsheet and a
SQL Server database. The DTS packages implemented are shown in Figure 6.14 and
Figure 6.15 respectively.
76
Figur e 6.14 Sales DTS Package
Figur e 6.15 Shipping DTS Package
77
The arrow from delimited sales text file to DW Data Source indicates the
transformation from the text file to the “SalesFact” table. The transformation is a copy
column transformation with the details shown in Figure 6.16.
Figur e 6.16 Tr ansfor mation details for delimited text file
Transformations from Microsoft® Excel and Microsoft® Access are similar to
transformation from the delimited text file. Transformation from the SQL Server
database is a little different. The table with the data is not loaded as it is, but with a
custom query. The detail of this transformation is shown Figure 6.17. As seen in the
figure, the transformation source is a custom Transact-SQL query with grouping and
aggregation of data.
78
Figur e 6.17 Tr ansact-SQL quer y as the tr ansfor mation sour ce
Another feature of DTS is lookup queries. Lookup queries allow running queries
and stored procedures against other connections besides the source and destination. For
example, by using a lookup query, you can make a separate connection during a query
and include data from that connection in the destination table. Lookup queries are
especially useful in validating input data before loading it.
After the populating data from different sources and transforming and loading the
data into the DW using DTS, we need some client application for end users to enable
them query the DW. In the sample implementation I used Microsoft® Excel and
Microsoft® Data Analyzer as the client applications and mainly the pivot table and pivot
chart technologies. The Figure 6.18 shows a snapshot of pivot chart view of the sales
cube.
79
Figur e 6.18 Pivot Ch ar t using Excel as client
The Figure 6.19 shows pivot table view of the sales cube.
Figur e 6.19 Pivot Table using Excel as client
80
The Figure 6.20 shows the sales cube using Microsoft® Data Analyzer.
Figur e 6.20 Data Analyzer as client
Microsoft® Data Analyzer is business-intelligence software that enables you to
apply the intelligence of an organization to the challenges of displaying and analyzing
data in a quick and meaningful manner. Data Analyzer accomplishes this by giving the
user a complete overview in one screen, which helps the user to quickly find hidden
problems, opportunities, and trends. By using Data Analyzer, non-technical business
users can get answers to their questions immediately and independently, which puts
knowledge directly into the hands of the people who need it most — decision-makers at
all levels in the organization. Data Analyzer provides a number of advantages for
displaying and analyzing the data:

A complete overview on a single screen replaces masses of grids, graphs, and
reports.
81

Multidimensional views show relationships throughout all aspects of your
business.

Customizable displays guide the user to hidden problems, opportunities, and
trends.

The dynamic use of color highlights anomalies.

Saving and reporting functions allow the user to save views for future use and
export them to Microsoft® PowerPoint or Microsoft® Excel.

Power users can do more advanced analysis in less time.
82
CHAPTER 7
CONCLUSIONS AND FUTURE WORK
Successful data management is an important factor in developing support systems
for the decision-making process. Traditional database systems, called operational or
transactional, do not satisfy the requirements for data analysis of the decision-making
users. An operational database supports daily business operations and the primary
concern of such database is to ensure concurrent access and recovery techniques that
guarantee data consistency. Operational databases contain detailed data, do not include
historical data, and since they are usually highly normalized, they perform poorly for
complex queries that need to join many relational tables or to aggregate large volumes of
data.
A DW represents a large repository of integrated and historical data needed to
support the decision-making process. The structure of a DW is based on a
multidimensional model. This model includes measures that are important for analysis,
dimensions allowing the decision-making users to see these measures from different
perspectives, and hierarchies supporting the presentation of detailed or summarized
measures. The characteristics of a multidimensional model specified for the DW can be
applied for a smaller structure, a data mart, which is different from the DW in the scope
of its analysis. A data mart refers to a part of an organization and contains limited
amount of data.
83
DWs have become the main technology for DSS. DSSs require not only the data
repository represented by DW, but also the tools that allow analysing data. These tools
include different kinds of applications; for example, software that include statistics and
data mining techniques offers complex analysis for a large volume of data to identify
profiles, behaviour, and tendencies. On the other hand, OLAP tools can manage high
volumes of historical data allowing for dynamic data manipulations and flexible
interactions with the end-users through the drill-down, roll-up, pivoting, and slicingdicing operations. Furthermore, OLAP tools are based on multidimensional concepts
similar to DW multidimensional model using for it measures, dimensions, and
hierarchies. If the DW data structure has a well-defined multidimensional model, it is
easier to fully exploit OLAP tools capabilities.
In this thesis, widely accepted conceptual and logical design approaches in DW
design are discussed. In the conceptual design phase DF, starER, ME/R and OOMD
design models are compared. OO design model is significantly better than the other
design approaches. OOMD supports conceptual design phase with a rich set of diagrams
that enables the designer model all the business information and requirements using a
case tool with UML. OOMD design model meets the following factors while the others
lack one or more:

Additivity of measures

Many-to-many relationships with dimensions

Derived measures

Nonstrict and complete classification hierarchies

Categorization of dimensions (specialization/generalization)

Graphic notation

Specifying user requirements

Case tool support
In the logical design phase flat, terraced, star, fact constellation, galaxy, snowflake,
star cluster and starflake schemas are discussed. Among these logical design models,
star schema, snowflake schema and the fact constellation schema are the mostly used
models commercially. These three models are compared in terms of efficiency, usability,
84
reusability and flexibility quality factors among which efficiency is the most important
one considering DW modeling. Considering these factors and the requirements of the
business and considering the trade-off between redundancy and the query performance,
either snowflake or star schema may be the best choice in the design.
There are CASE tools that enable a user to design DW architecture. Current
commercial CASE tools have great design options to model that enable modelers to
model databases and software solutions even in the enterprise level. Unfortunately, the
development of the CASE tools on the data warehousing area is not as mature as the
development in ER and software modeling areas. Very few commercially available tools
may help in designing data warehousing solutions and may still not cover the
requirements you need. A CASE tool without basing the modeling notation on UML
may never cover the needs of data warehousing design. Note that, the main purpose of
UML is to “represent business rules”.
Unfortunately, a case tool for a complete data warehousing design is not available.
But there are still solutions using the existing CASE tools in data warehousing arena.
Two of the commercial CASE tools that support OOMD design are Microsoft® Visio
and Rational Rose. The sample OOMD model in the thesis is implemented using
Microsoft® Visio. Likewise, there are a number of OLAP Servers like Microsoft® SQL
Server Analysis Services, Hyperion Essbase, PowerPlay, DB2 OLAP Server. As
mentioned in the thesis, data warehousing is a complete process starting with data
acquisition, designing and storage of data and finally enabling end users access this data.
It is important for a platform to be able to offer a complete solution in data warehousing.
In this thesis, the data warehousing application is implemented using Microsoft
technologies. For the data acquisition phase Microsoft® DTS, for design and storage
phase Microsoft® SQL Server Analysis Services and finally for end user access
Microsoft® Excel and Microsoft® Data Analyzer are used.
7.1. Contr ibutions of the Thesis
This thesis contributes to both theory and practice of data warehousing. The data
warehouse design models and approaches in the literature are researched and grouped
85
according to the phase in the project development cycle. These models are refined
according to the acceptance in the literature.
The first contribution of the thesis is the comparison of methodologies namely E/R,
DM and OO. The second contribution of the thesis is the comparison of the conceptual
design models. The third contribution of the thesis is the comparison of the logical
design models in terms of the quality factors. The fourth contribution of the thesis is a
case study covering the phases of a data warehousing solution. In addition, issues and
problems identified in this thesis that might impact the data warehouse implementation
will enable project managers to develop effective strategies for successfully
implementing data warehouses.
According to my research, there is no complete study in literature on DW models
providing a mapping of models to development phases and giving a comparison of the
models according to these phases. Also, very few articles point to an implementation
covering all phases of data warehousing. So, the last contribution of the thesis is
providing a complete study on these missing points.
7.2. Futur e Wor k
One possible future work may be comparing the pyhsical design models for a data
warehousing solutions and extend the case study to cover these physical design
approaches.
Another future work may be implementing a more complex case study using real
world application data, perform performance tests using the three logical models
compared to support the comparison on logical design models presented in the thesis.
Another future work may be improving the comparison of logical design models by
both covering more model and more quality factor in the comparison.
A case tool for a complete data warehousing design is not available. One future
work may be implementing of a case tool meeting the requirements of a data
warehousing solution.
86
REFERENCES
[1] Romm M., Introduction to Data Warehousing, San Diego SQL User Group
[2] Goyal N., Introduction to Data Warehousing, BITS, Pilani Lecture Notes
[3] Franconi E., Introduction to Data Warehousing, Lecture
http://www.inf.unibz.it/~franconi/teaching/2002/cs636/2 ,2002
Notes,
[4] Pang L., Data Warehousing and Data Mining, Leslie Pang Web Site and Lecturer
Notes
[5] Gatziu S. and Vavouras A., Data Warehousing: Concepts and Mechanisms, 1999
[6] Thomas Connolly & Carolyn Begg., “Database Systems, 3th Edition”, AddisonWesley, 2002
[7] Gatierrez A. and Marotta A., An Overview of Data Warehouse Design
Approaches and Techniques, Uruguay, 2000
[8] Reed Jacobson., “Microsoft® SQL Server 2000 Analysis Services”, ISBN 0-73560904-7, 2000
[9] Rizzi S., Open Problems in Data Warehousing., http://sunsite.informatik.rwthaachen.de/Publications/CEUR-WS/Vol-77/ DMDW 2003, Berlin, Germany
[10] J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Chapter2:
Data Warehouse and OLAP Technology for Data Mining, Barnes & Nobles, 2000
[11] W. H. Inmon, “Building the Data Warehouse, 3th Edition”, John Wiley, 2002
87
[12] Moody D. L. and Kortink M. A. R., From Enterprise Models to Dimensional
Models: Methodology for Data Warehouse and Data Mart Design,
http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS//Vol-28/
http://sunsite.informatik.rwth
DMDW 2000 , Stockholm, Sweden
[13] Tryfona N., Busborg F., Christiansen J. G., starER: A Conceptual Model for Data
Warehouse Design, Proceeding of the ACM 2nd International Workshop Data
Warehousing and OLAP (DOLAP99), 1999
[14] Sapia C., Blaschka M., Höfling G., Dinter B., Extending the E/R Model for the
Multidimensional Paradigm, Proceeding 1st International Workshop on Data
Warehousing and Data Mining (DWDM98), 1998
[15] Golfarelli M., Maio D., Rizzi S., Conceptual Design of Data Warehouses from
E/R Schemas, Proceeding of the 31st Hawaii International Conference on System
Sciences (HICSS-31), Vol. VII,1998
[16] Golfarelli M., Maio D., Rizzi S., The Dimensional Fact Model: A Conceptual
Model For Data Warehouses, International Journal of Cooperative Information
Systems (IJCIS), Vol. 7, 1998
[17] Golfarelli M, Rizzi S., A Methodological Framework for Data Warehouse
Design, Proceeding of the ACM DOLAP98 Workshop, 1998
[18] Lujan-Mora S., Trujillo J., Song I., Multidimensional Modeling with UML
Package Diagrams, 21st International Conference on Conceptual Modeling
(ER2002), 2002
[19] Trujillo J., Palomar M., An Object Oriented Approach to Multidimensional
Database Conceptual Modeling (OOMD) , Proceeding 1st International
Workshop on Data Warehousing and OLAP (DOLAP98), 1998
[20] Kimball R., http://www.dbmsmag.com/9708d15.html “A Dimensional Modeling
Manifesto”, DBMS Magazine, Aug 1997
[21] Kimball R., “The Data Warehouse Toolkit”, John Wiley, 1996
[22] Martyn T., Reconsidering Multi-Dimensional Schemas, SIGMOD Record, Vol.
33, No. 1, 2004
[23] Elmasri R., Navathe S., “Fundamentals of Database Systems”, 3rd Edition,
Addison-Wesley, 2000
[24] Ballard C., Herreman D., Schau D., Bell R., Kim E., and Valencic A., “Data
Modeling Techniques for Data Warehousing”, IBM Redbook, IBM International
Technical Support Organization, 1998
88
[25] Firestone J., Object-Oriented Data Warehousing, 1997
[26] Kimball R., Enforcing the Rules, 2000 ,
http://www.intelligententerprise.com/000818/webhouse.jhtml?_requestid=380244
[27] Kimball R., The Software Developer in Us, 2000,
http://www.intelligententerprise.com/000908/webhouse.jhtml
http://www.intelligententerprise.com/000908/webho
[28] Microsoft Developer Network (MSDN) Library, XML Web Services Overview,
October 2004
[29] Hahn K., Sapia C., and Blaschka M., Automatically Generating OLAP Schemata
from Conceptual Graphical Models, Proceedings ACM 3rd International
Workshop Data Warehousing and OLAP (DOLAP 2000), 2000
[30] Mora-Lujan S., Multidimensional Modeling Using UML and XML, Proceedings
16th European Conference on Object-Oriented Programming (ECOOP 2002),
2002
[31] Golfarelli M., Rizzi S., WAND: A Case Tool for Data Warehouse Design, Demo
Proceedings of The 17th International Conference on Data Engineering (ICDE
2001), 2001
[32] Chaudhuri S., Dayal U., An Overview of Data Warehousing and OLAP
Technology, ACM Sigmod Record, vol.26, 1997
[33] Golfarelli M., Rizzi S., Designing the Data Warehouse: Key Steps and Crucial
Issues, Journal of Computer Science and Information Management, 1999
[34] Phipps C., Davis K., Automating Data Warehouse Conceptual Schema Design
and Evaluation, DMDW’02, 2002
[35] Peralta V., Marotta A., Ruggia R., Towards the Automation of Data Warehouse
Design, 2003
[36] Batini C., Ceri S., Navathe S., “Conceptual Database Design-An Entity
Relationship Approach”, Addison-Wesley, 1992
[37] Abello A., Samos J., Saltor F., A Data Warehouse Multidimensional Data Models
Classification, Technical Report, 2000
[38] Abello A., Samos J., Saltor F., A Framework for the Classification and
Description of Multidimensional Data Models, Database and Expert Systems
Applications, 12th International Conference, 2001
[39] Teklitz F., The Simplification of Data Warehouse Design, Sybase, 2000
89
[40] Prosser A., Ossimitz M., Data Warehouse Management, University of Economics
and Business Admin., Vienna, 2000
[41] Ahmad I., Azhar S., Data Warehousing in Construction: From Conception to
Application, First International Conference on Construction in the 21st Century
(CITC2002) “Challenges and Opportunities in Management and Technology” ,
2002
[42] Kimball R., Letting the Users Sleep, Part 1, DBMS, 1996,
http://www.dbmsmag.com/9612d05.html
http://www.dbmsmag.com/9612d05.ht
[43] Kimball R., Letting the Users Sleep, Part 2, DBMS, 1997,
http://www.dbmsmag.com/9701d05.html
90
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