DatabasesAndDatawarehousingconcepts1.ppt

Databases and data warehouses

1. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 1
Chapter 2
Databases & Data Warehouses

2. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 2
Outline

Database Concepts

Steps in Database Design

Entity-Relationship Model

Logical Database Design

Relational Model

Object-Relational Model

Physical Database Design

Data Warehouse Concepts

Logical Data Warehouse Design

Physical Data Warehouse Design

Data Warehouse Architecture

3. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 3
Steps in Database Design

Requirements specification: Collects information about
users’ needs with respect to the database system

Conceptual design: Builds a user-oriented representation
of the database without any implementation
considerations

Logical design: Translates the conceptual schema from
the previous phase into an implementation model
common to several DBMSs, e.g., relational or object-
relational

Physical design: Customizes the logical schema from the
previous phase to a particular platform, e.g., Oracle or
SQL Server

4. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 4
Entity Relationship Model: University
Example
Project
Project id
Project acronym
Project name
Description (0,1)
Start date
End date
Budget
Funding agency
(0,n)
Academic staff
Employee no
Name
First name
Last name
Address
Email
Homepage
Research areas (1,n)
Course id
Course name
Level
(1,n)(0,n)
(partial,exclusive)
(0,n)
Start date
End date
Is a prereqHas prereq
Course
Professor
Status
Tenure date (0,1)
/No courses
Assistant
Thesis title
Thesis description
(0,n)(1,1)
Advisor
Semester
Year
Homepage (0,1)
Participates
Prerequisite
(1,1)
(1,n)
Section
CouSec
(0,n)(1,1)
Teaches

5. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 5
ER Model: Entity and Relationship Types (1)

Entity Relationship Model: Most often used conceptual
model for database design

Entity type: Represents a set of real-world objects of
interest to an application

Entity: object belonging to an entity type

Relationship type: Represents an association between
objects

Relationship: association belonging to a relationship type

Role: Participation of an entity type in a relationship type

Cardinalities in a role: Minimum and maximum number
of times that the entity may participate in the
relationship type

6. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 6
ER Model: Entity and Relationship Types (2)

Types of roles

Optional vs mandatory: minimum cardinality 0 vs 1

Monovalued vs multivalued: maximum cardinality 1 vs n

Relationship types

Binary vs n-ary: two vs more than two participating entity types

Binary relationship types:

One-to-one, one-to-many, or many-to-many depending on the
maximum cardinality of the two roles

Recursive relationship types: the same entity type
participates more than once in the relationship type

Role names are then necessary to distinguish different roles

7. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 7
ER Model: Attributes

Attributes: Structural characteristics describing objects
and relationships

Attributes have cardinalities, as for roles

Depending on cardinalities, attributes may be

Optional vs mandatory

Monovalued vs multivalued

Complex attributes: Attributes composed of other
attributes

Derived attributes: Their value for each instance may be
calculated from other elements of the schema

8. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 8
ER Model: Identifiers and Weak Entity Types

Identifier or key: One or several attributes that uniquely
identify a particular object

Weak entity type: Do not have identifier of their own

Regular entity types are called strong entity types

A weak entity type is dependent on the existence of
another entity type, called the identifying or owner entity
type

An identifying relationship type relates a weak entity
type to its owner

Normal relationship types are called regular relationship types

Partial key: Set of attributes that can uniquely identify
weak entities related to the same owner entity

9. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 9
ER Model: Generalization (1)

Generalization or is-a relationship: Relates perspectives
of the same concept at different abstraction levels

Supertype: Entity at higher abstraction level

Subtype: Entity at lower abstraction level

Population inclusion: Every instance of the subtype is
also an instance of the supertype

Inheritance: All characteristics (e.g., attributes, roles) of
the supertype are inherited by the subtype

Substitutability: Each time an instance of a supertype is
required, an instance of the subtype can be used instead

10. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 10
ER Model: Generalization (2)

Several generalization relationships accounting for the
same criterion can be grouped

Types of generalizations

Total vs partial: depending on whether every instance of the
supertype is also an instance of one of the subtypes

Disjoint vs overlapping: depending on whether an instance may
belong to one or several subtypes

Multiple inheritance: A subtype that has several
supertypes

May induce conflicts when a property of the same name is
inherited from two supertypes

11. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 11
ER Model: Summary of Notation
Professor
Identifier
Other attributes
Attributes
Teaches
Partial Identifier
Other Attributes
Section
(0,1)
(1,1)
(0,n)
(1,n)
Simple attribute
Complex attribute
Attribute 1
Attribute 2
Optional att. (0,1)
Multivalued attr. (1,n)
(1,1)
role name
Entity Type Relationship Type
Weak Entity Type
Attribute typesRole Cardinalities
Identifying
Relationship Type
Generalization
CouSec
(total,exclusive)
(partial,exclusive)
(total,overlapping)
(partial,overlapping)
Generalization types

12. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 12
Relational Model: University Example
Professor
Employee no
Status
Tenure date (0,1)
No courses
Academic staff
Employee no
First name
Last name
Address
Email
Homepage
Academic staff
Research area
Employee no
Research area
Course id
Course name
Level
Course
Course id
Has prereq
Prerequisite
Project
Project id
Project acronym
Project name
Description (0,1)
Start date
End date
Budget
Funding agency
Employee no
Project id
Start date
End date
Participates
Assistant
Employee no
Thesis title
Thesis description
Advisor
Course id
Semester
Year
Homepage (0,1)
Employee no
Section

13. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 13
Relational Model (1)

Based on a simple data structure

A relation or table, composed of attributes or columns

Each attribute is defined over a domain or data type

Typical domains: integer, float, date, string, …

Tuple or row: element of the table

Provides several declarative integrity constraints

Not null attribute: a value for the attribute must be
provided

Key: column(s) that uniquely identify one row of the table

Simple vs composite key: depending on whether key is composed
of one or several columns

Primary vs alternate key: one of the keys must be chosen as
primary by the designer, the others are alternate keys

14. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 14
Relational Model (2)

Referential integrity

Defines a link between two tables (or twice the same one)

A set of attributes in one table (foreign key) references the
primary key of the other table

Values in the foreign key columns must also exist in the primary
key of the referenced table

Check constraint: defines a predicate that must be valid
when inserting or updating a tuple in a relation

Predicate can only involve the tuple being inserted/deleted

All other constraints must be expressed by triggers: an
event-condition-action rule that is automatically
activated when a relation is updated

15. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 15
Translating from ER to Relational Schemas
(1)

Rule 1: A strong entity type is associated with a table

Table contains the simple monovalued attributes and the simple
component of the monovalued complex attributes of the entity
type

Table also defines not null constraints for mandatory attributes

Identifier of the entity type define the primary key of the table
Academic staff
Employee no
Name
First name
Last name
Address
Email
Homepage
Research areas (1,n)
Academic staff
Employee no
First name
Last name
Address
Email
Homepage

16. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 16
Translating from ER to Relational Schemas
(2)

Rule 2: A weak entity type is transformed as a strong
entity type, but the associated table also contains the
identifier of the owner entity

A referential integrity constraint relates this identifier to the table
associated with the owner entity type

Primary key of the table: partial identifier of the weak entity type
+ identifier of the owner entity type
Semester
Year
...
Section Course
(1,n)(1,1)
Course id
...
CouSec
Course id
Semester
Year
...
Section

17. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 17
Translating from ER to Relational Schemas
(3)

Rule 3: A binary one-to-one relationship type is
represented by including the identifier of one of the
participating entity types in the table associated to the
other entity type

A referential integrity constraint relates this identifier to the table
associated with the corresponding entity type

Simple monovalued attributes and simple components of
monovalued complex attributes are also included in the table

Not null constraint for mandatory attributes are also defined
Department
...
Professor
(0,1)(1,1)
Dean
...
Department
...
Dean

18. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 18
Translating from ER to Relational Schemas
(4)

Rule 4: A binary one-to-many relationship type is
represented by including the identifier of the entity type
with cardinality 1 in the table associated to the other
entity type

A corresponding referential integrity constraint is added

Attributes and not null constraints are also added as in previous
rules
Assistant
...
Professor
(0,n)(1,1)
Advisor
...
Assistant
...
Advisor

19. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 19
Translating from ER to Relational Schemas
(5)

Rule 5: A binary many-to-many relationship type or an n-
ary relationship type is associated with a table containing
the identifiers of the entity types that participate in all its
roles

Corresponding referential integrity constraints are added

Attributes and not null constraints are also added as in previous
rules

Identifier if any is the key of the relation, but the combination of
all role identifiers can be used as a key
Academic staff Project
Project id
...
Employee no
...
(1,n)(0,n)
Start date
End date
Participates
Employee no
Project id
Start date
End date
Participates

20. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 20
Translating from ER to Relational Schemas
(6)

Rule 6: A multivalued attribute is associated with a table
containing the identifier of the entity or relationship type
to which it belongs

Corresponding referential integrity constraint is added

Primary key of table is composed of all its attributes
Academic staff
...
Research areas (1,n)
Academic staff
Research area
Employee no
Research area

21. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 21
Translating from ER to Relational Schemas
(7)

Rule 7: A generalization can be translated in 3 ways

The supertype and the subtype are associated with tables
containing the identifier of the entity type. There is a referential
integrity constraint from the table of the subtype to the table of
the subtype
Academic staff
Employee no
Name
...
Professor
Status
...
Assistant
Thesis title
...
Academic staff
Employee no
Name
...
Professor
Employee no
Status
...
Assistant
Employee no
Thesis title
...

22. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 22
Translating from ER to Relational Schemas
(8)

Rule 7 (cont.):

Only the supertype is associated with a table, which contains also
the attributes of the subtype (these are optional attributes)

Only the subtype is associated with a table containing all
attributes of the subtype and the supertype
Academic staff
Employee no
Name
…
Thesis title (0,1)
...
Status
...
Professor
Employee no
Name
…
Status
...
Assistant
Employee no
Name
…
Thesis title
...

23. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 23
Defining Relational Schemas in SQL
create table AcademicStaff as (
EmployeeNo integer primary key,
FirstName character varying (30) not null,
LastName character varying (30) not null,
Address character varying (50) not null,
Email character varying (30) not null,
Homepage character varying (64) not null );
create table AcademicStaffResearchArea as (
EmployeeNo integer not null,
ResearchArea character varying (30) not null,
primary key (EmployeeNo,ResearchArea),
foreign key EmployeeNo references AcademicStaff(EmployeeNo) );
…

24. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 24
Redundancies and Update Anomalies
Relation with redundancies may induce anomalies in the
presence of insertions, updates, and deletions

In relation Participates the information about a project is repeated
for each staff member who works on that project

In relation Affiliation the information about a research area is
repeated for all departments to which the staff member is
affiliated
Dependencies and normal forms are used to describe
such anomalies
Employee no
Project id
Start date
End date
Project acronym
Project name
Participates Affiliation
Employee no
Research area
Department id

25. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 25
Functional Dependencies

Given a relation R and to sets of attributes X and Y in R, a
functional dependency X Y holds if in all tuples of the
relation, each value of X is associated with at most one
value of Y

In Participates above, Project Id {Project acronym, Project name}

A key is a particular case of a functional dependency

A prime attribute is an attribute that is part of a key

A full functional dependency is a dependency X Y in
which the removal of an attribute from X invalidates the
dependency

A dependency X Z is transitive if there is a set of
attributes Y such that X Y and Y Z hold

26. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 26
Multivalued Dependencies

Given a relation R and to sets of attributes X and Y in R, a
multivalued dependency X Y holds if the value of X
determines a set of values for Y, independently of any
other attributes

In Affiliation above, Employee no Research area and
Employee no Department Id

A multivalued dependency X Y is trivial if either Y
X or X Y = R, otherwise it is nontrivial

27. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 27
Normal Forms (1)

Normal Form: Integrity constraint certifying that a
relation schema satisfies particular properties

In the relational model the relations are in first normal
form since all attributes are atomic and monovalues

A relation schema is in the second normal form if every
nonprime attribute is functionally dependent on every
key
Employee no
Project id
Start date
End date
Project acronym
Project name
Participates
Employee no
Project id
Start date
End date
Participates
Project id
Project acronym
Project name
Project

28. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 28
Normal Forms (2)

A relation is in the third normal form if it is in the second
normal form and there are no transitive dependencies
between a key and a nonprime attribute

A relation is in the Boyce-Codd normal form if for every
nontrivial dependency X  Y, X is a key or contains a key
Assistant
Employee no
Thesis title
Thesis description
Advisor id
Advisor first name
Advisor last name
Assistant
Employee no
Thesis title
Thesis description
Advisor id
Professor
Employee no
First name
Last name
Employee no
Project id
Start date
End date
Location
Participates
Employee no
Project id
Start date
End date
Participates
Location
Project id
Location

29. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 29
Normal Forms (3)

A relation is in the fourth normal form if for every
nontrivial dependency X Y, X is a key or contains a
key
Affiliation
Employee no
Research area
Department id
Affiliation
Employee no
Research area
Research
Employee no
Research area

30. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 30
Weaknesses of the Relational Model

It provides a very simple data structure, which disallows
multivalued and complex attributes

complex objects are split into several tables performance
problems

The set of types provided by relational DBMSs is very
restrictive (integer, float, string, date, …)

Insufficient for complex application domains

There is no integration of operations with data
structures

It is not possible to directly reference an object by use of
a surrogate or a pointer

links are based on comparison of values performance
problems

31. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 31
Object-Relational Model: Motivations
Preserves the foundations of the relational model
Organizes data using an object model

Allow attributes to have complex types groups related facts
into a single row
Extensions

Complex and/or multivalued attributes

User-defined types with associated methods

System-generated identifiers

Inheritance among types
Defined in the SQL:1999 and SQL:2003 standards
Current DBMSs (Oracle, …) introduced object-relational
extensions, but do not necessarily comply with the
standard

32. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 32
Object-Relational Model: Definition (1)

Allow composite types, in addition to predefined types

Complex values may be stored in a column of a table

Row type: structured values (i.e., composite attributes)

Array type: variable-sized vectors of values

Multiset type: Unordered collection of values, with duplicates
permitted (bags)

Composite types can be nested

This is considered an “advanced feature” in the standard

33. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 33
Object-Relational Model: Definition (2)

Two sorts of user-defined types

Distinct types: Represented internally by a predefined type,
but cannot be mixed in expressions

Does not make sense to compare a person’s name and a SSN

Structured user-defined types:  class declarations in OO
languages

May have attributes, which can be of any SQL type

May have methods, which can be instance or static (class) methods

Attributes are encapsulated through observer and mutator functions

Both of them can be used as a domain of a column, of an
attribute of another type, or a table

Comparison/ordering of values: with user-defined methods

34. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 34
Object-Relational Model: Definition (3)
SQL:2003 supports single inheritance:

A subtype inherits attributes and methods from its supertype
A subtype can

Define additional attributes and methods

Overload inherited methods: provide another definition with a
different signature

Override inherited methods: provide another implementation
with a the same signature
Final types: cannot have subtypes
Final methods: cannot be redefined
Instantiable type: includes constructor methods for
creating instances

35. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 35
Object-Relational Model: Definition (4)

Two types of tables

Relational tables: usual ones, where domains for
attributes are all predefined or user-defined types

Typed tables: defined on structured user-defined types

Constraints (e.g. keys) defined in the table declaration

Have a self-referencing column that contains identifiers
(surrogates) generated by the DBMS

Row identifiers can be used for establishing links between tables

Ref type: values are the unique identifiers

Always associated with a specified structured type

36. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 36
Object-Relational Model: University Example
Academic staff Type
Employee no
Name
First name
Last name
Address
Email
Homepage
Research areas (1,n)
Participates (0,n)
Professor Type
Status
Tenure date (0,1)
No courses
Advises (0,n)
Teaches (0,n)
Project Type
Project id
Project acronym
Project name
Description (0,1)
Start date
End date
Budget
Funding agency
Participates (1,n)
Semester
Year
Homepage (0,1)
Professor
Course
Section Type
Employee
Project
Start date
End date
Participates Type
Assistant Type
Thesis title
Thesis description
Advisor
Course id
Course name
Level
Sections (1,n)
Has prereq (0,n)
Is a prereq (0,n)
Course Type

37. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 37
Translating from ER to OR Schemas (1)

Rule 1: A strong entity type is associated with a
structured type containing all its attributes

Multivalued attributes defined with the array or the multiset
type

Composite attributes defined with the row or the structured
type

A table of this structured type must be declared

Table defines not null constraints for mandatory attributes

Identifier of the entity type define the primary key of the table
Academic staff
Employee no
Name
First name
Last name
Address
Email
Homepage
Research areas (1,n)
Academic staff Type
Employee no
Name
First name
Last name
Address
Email
Homepage
Research areas (1,n)

38. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 38
Translating from ER to OR Schemas (2)

Rule 2: A weak entity type is transformed as a strong
entity type, but the structured type contains the
identifier of its owner
Semester
Year
Homepage (0,1)
Section Course
(1,n)(1,1)
Course id
...
CouSec
Semester
Year
Homepage (0,1)
Course
Section Type Course Type
Course id
...

39. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 39
Translating from ER to OR Schemas (3)

Rule 3: A regular relationship type is associated with
structured type containing the identifiers of all
participating entity types, and all attributes of the
relationship

A table of this structured type must be declared

Table defines not null constraints for mandatory attributes

Set of identifiers of entity types define the primary key of the
table

Inverse references may also be defined
Academic staff Project
Project id
...
Employee no
...
(1,n)(0,n)
Start date
End date
Participates
Acad. staff
Project
Start date
End date
Participates
Type
Academic staff
Type
…
Participates (0,n)
Project
Type
…
Participates (1,n)

40. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 40
Translating from ER to OR Schemas (4)

Rule 4: Only single inheritance is supported by the model

multiple inheritance must be removed by transforming
some generalization links into binary one-to-one
relationships, to which Rule 3 is applied
PhD Student
Thesis title
...
Assistant
Assistant Type
...
Student
Major
...
PhD Student
Thesis title
…
Student
Assistant
Assistant Type
...
Student
Major
...
PhD Student

41. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 41
Defining Object-Relational Schemas in SQL
create type AcademicStaffType as (
EmployeeNo integer,
Name row (
FirstName character varying (30),
LastName character varying (30) ),
Address character varying (50),
Email character varying (30),
Homepage character varying (64),
ResearchAreas character varying (30) multiset,
Participates ref(ParticipatesType) multiset scope Participates
references are checked )
ref is system generated;
create table AcademicStaff of AcademicStaffType (
constraint AcademicStaffPK primary key (EmployeeNo),
Email with options constraint AcademicStaffEmailNN Email not null,
ref is oid system generated );
…

42. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 42
Physical Database Design

Specifies how database records are stored, accessed,
and related in order to ensure adequate performance of
a database application

Requires to know the specificities of an application,
properties of data, and usage patterns

Involves analyzing the transactions/queries that run
frequently, that are critical, and the periods of time in
which there will be a high demand on the database (peak
load)

43. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 43
Performance of Database Applications

Factors for measuring performance of database
applications

Transaction throughput: # transaction processed in a time interval

Response time: Elapsed time for the completion of a transaction

Disk storage: Space required to store the database file

A compromise has to be made among these factors

Space-time trade-off: Reduce time to perform an operation by using
more space, and vice versa

Query-update trade-off: Access to data can be more efficient by
imposing some structure upon it

However the more elaborate the structure, the more time is needed
to built it and maintain it when content changes

After initial physical design is done, it is necessary to
monitor it and tune it

44. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 44
Data Organization

A database is organized in secondary storage into files,
each composed of records, at their turn composed of
fields

In a disk, data is stored in disk blocks (pages)

Set by the operating system during disk formatting

Transfer of data between main memory and disk takes
place in units of disk blocks

DBMSs store data on database blocks (pages)

Selection of a database block depends on several issues

Locking granularity may be at the block level, not the record level

For disk efficiency, database block size must be equal to or be a
multiple of the disk block size

45. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 45
File Organization

Arrangement of data in a file into records and blocks

Heap files (unordered files): Records placed in the file in
the order as they are inserted

Efficient insertions, slow retrieval

Sequential files (ordered files): Records sorted on the
values of ordering fields

Fast retrieval, slow inserting and deleting

Hash files: Use a hash function that calculates the block
(bucket) of a record based on one or several fields

Collision: Bucket is filled and a new record must be inserted

Fastest retrieval, but collision management degrades
performance

46. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 46
Indexes

Additional access structures that speed up retrieval of
records in response to search conditions

Provide alternative ways of accessing the records based
on the indexing fields on which the index is constructed

Many different types of indexes

Clustering vs nonclustering: Whether records in the file are
physically ordered according to the fields of the index

Single column vs multiple column, depending on the number of
indexing files

Unique vs nonunique: Whether duplicate values are allowed

47. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 47
Indexes, Clustering

Types of indexes (cont.)

Sparse or dense: Whether there are index records for all search
values

Single-level vs multilevel: Whether an index is split in several
smaller indexes, with an index to these indexes

Multi-level indexes often implemented by using B-trees of B
+
-trees

Clustering: Tables physically stored together as they
share common columns (cluster key) and are often used
together

Improves data retrieval

Cluster key stored only once  storage efficiency

48. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 48
Outline

Database Concepts

Steps in Database Design

Entity-Relationship Model

Logical Database Design

Relational Model

Object-Relational Model

Physical Database Design

Data Warehouse Concepts

Logical Data Warehouse Design

Physical Data Warehouse Design

Data Warehouse Architecture

49. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 49
Data Warehouses: Definition (1)

Operational databases (online transaction processing
systems or OLTP), are not suitable for data analysis

Contain detailed data, do not include historical data, perform
poorly for complex queries due to normalization

Data warehouses address requirements of decision-
making users

A data warehouse is a collection of subject-oriented,
integrated, nonvolatile, and time-varying data to support
data management decisions

Subject-oriented: focus on particular subjects of analysis
(e.g., purchase, inventory, and sales in a retail company)

Operational databases focus on specific functions of an
application

50. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 50
Data Warehouses: Definition (2)
Integrated: Join together data from several operational
and external systems problems due to differences in
data definition and content

In operational DBs these problems are solved in the design phase
Nonvolatile: Data modification and removal is disallowed
scope of the data is long period of time

Operational DBs keep data for a short period of time, as required
for daily operations
Time-varying: Keep different values for the same informa-
tion and the time when changes to these values occurred

Operational DBs typically do not have temporal support
Online analytical processing (OLAP): Allows decision-
making users to perform interactive analysis of data

51. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 51
Multidimensional Model

DWs and OLAP use a multidimensional view of data

Represented as a data cube or an hypercube

Dimensions: Perspectives for analyzing data

Cells (facts): Contain measures, values that are to be analyzed
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
3226
14
2014 31
12202433
24182814
33252325
measure
values
dimensions
35
27

52. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 52
Hierarchies

Data granularity: Level of detail of measures

Data analyzed at different granularities (abstraction
levels)

Hierarchies relate low-level (detailed) concepts to higher-
level (general concepts)

Example: Store – City – Region/Province – Country

Given two related levels in a hierarchy, lower level is
called child, higher level is called parent

Instances of these levels are called members

53. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 53
Kinds of Hierarchies
Balanced: Same number of levels from leaf members to root
Noncovering (ragged): Parent of some members does not
belong to the level immediately above

E.g., small countries do not have Region/Province level
Store level
ParisCity level
Region/
Province level
Country level
Store 1 Store 2
Ile-de-France
France
Nice
Store 3
Store dimension
Rome
Store 10Store 11
Milan
Store 12
Lazio
Italy
All
......
...
...
Lombardy
Provence-Alpes-
Côte d'Azur

54. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 54
Kinds of Hierarchies

Unbalanced: May not have instances at the lower levels

E.g., some cities do not have stores, sales are through
wholesalers

Parent-child (recursive): Involve twice the same level
Jottard
Hallez Berger
Nguyen
Vancamp Rochat
Weiss
Ivanov
Spiegel Praet
Pirlot Quintin
Claus
Lepage

55. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 55
Measure Aggregation and Summarizability

Measures are aggregated when using hierarchies for
visualizing data at different abstraction levels

Summarizability conditions ensure correct aggregation

Disjointness of instances: Grouping of instances in a level with
respect to the parent in the next level must result in disjoint sets

Completeness: All instances are included in the hierarchy and
each instance is related to one parent in the next level

Correct use of aggregation functions: Type of measures
determine the kind of aggregation functions that can be applied

56. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 56
Measure Classification: Additivity
Additive measures (flow or rate measures): Can be
meaningfully summarized using addition along all
dimensions

E.g., sales amount can be summarized when the hierarchies in
Store, Time, and Product dimensions are traversed
Semiadditive measures (stock or level measures): Can be
meaningfully summarized using addition along some
(not all) dimensions

E.g., inventory quantities, can be aggregated in the Store
dimension, but cannot be aggregated in the Time dimension
Nonadditive measures (value-per-unit measures): Cannot
be meaningfully summarized using addition along any
dimension

E.g., item price, cost per unit, exchange rate

57. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 57
Measure Classification: Aggregation
Complexity

Distributive measures: Defined by an aggregation function
that can be computed in a distributed way

Data is partitioned into n sets, aggregate function applied to each
set, aggregated value is computed by applying a function to these n
subaggregate values

E.g., count, sum, min, max

Algebraic measures: Defined by an aggregation function
that has a bounded number of arguments, each is obtained
by applying a distributive function

E.g., average, computed from sum and count, which are distributive

Holistic measures: Cannot be computed from other
subaggregate values

E.g., median, mode, rank

58. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 58
OLAP Operations: Roll up

Transforms detailed measures into summarized ones
when one moves up in a hierarchy
Roll-up to the Country level
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
323526
2714
2014 31
12202433
24182814
33252325
33
12
11
68
Q4
France
Italy
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
CDs
DVDsgames
3042
30
323526
2714
2014 31
57435139

59. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 59
OLAP Operations: Drill down

Opposite to the roll-up operation, i.e., it moves from a
more general level to a detailed level in a hierarchy
Drill-down to the
Month level
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
323526
2714
2014 31
12202433
24182814
33252325
7
4
8
13
...
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
...
Mar
Feb
Jan
Rome
CDs
DVDsgames
26
12
1046
84
...... ...
47810
8695
108118
1644 7Dec

60. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 60
OLAP Operations: Pivot or Rotate

Rotates the axes of a cube to provide an alternative
presentation of the data
21
28
11
14
Q4
Milan
Rome
Paris
Time (Quarter)
books
25
Q3Q2Q1
Nice
CDs
DVDs
games
2726
13
323533
1214
2324 18
10141220
35303231
18113547
S
t
o
r
e

(
C
i
t
y
)
Pivot
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
323526
2714
2014 31
12202433
24182814
33252325

61. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 61
OLAP Operations: Slice

Performs a selection on one dimension of a cube,
resulting in a subcube
Slice on Store.City = ‘Paris’
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
323526
2714
2014 31
12202433
24182814
33252325 21
12
11
35
Q4
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
CDs
DVDsgames
1018
30
323526
2714
2014 31

62. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 62
OLAP Operations: Dice

Defines a selection on two or more dimensions, thus
again defining a subcube
Dice on Store.Country = ‘France’
and Time.Quarter= ‘Q1’ or ‘Q2’
21
12
11
35
Q4
Paris
Nice
Milan
Product (Category)
T
i
m
e

(
Q
u
a
r
t
e
r
)
books
47
Q3
Q2
Q1
Rome
CDs
DVDsgames
1018
30
323526
2714
2014 31
12202433
24182814
33252325
21
11
35
Paris
Nice
Product (Category)
T
i
m
e
(
Q
u
a
r
t
e
r
)
books
Q2
Q1
CDs
DVDsgames
1018
302714
12202433

63. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 63
Other OLAP Operations

Drill-across: Executes queries involving more than one
cube, which share at least one dimension

E.g., compare sales data in a cube with purchasing data in
another one

Drill-through: Allows one to move from data at the
bottom level in a cube, to data in the operational
systems from which the cube was derived

E.g., for determining the reason for outlier values in a data cube

64. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 64
Implementations of a Multidimensional
Model

Relational OLAP (ROLAP): Store data in relational
databases, support extensions to SQL and special access
methods to efficiently implement the model and its
operations

Multidimensional OLAP (MOLAP): Store data in special
data structures (e.g., arrays) and implement OLAP
operations in these structures

Better performance than ROLAP for query and aggregation,
less storage capacity than ROLAP

Hybrid OLAP (HOLAP): Combine both technologies,

E.g., detailed data stored in relational databases, aggregations
kept in a separate MOLAP store

65. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 65
Logical Data Warehouse Design
In ROLAP systems, tables organized in specialized structures
Star schema: One fact table and a set of dimension tables

Referential integrity constraints between fact table and dimension
tables

Dimension tables may contain redundancy in the presence of
hierarchies
Snowflake schema: Avoids redundancy of star schemas by
normalizing dimension tables

Normalized tables optimize storage space, but decrease performance
Starflake schema: Combination of the star and snowflake
schemas, some dimensions normalized, other not
Constellation schema: Multiple fact tables that share
dimension tables

66. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 66
Logical DW Design: Star Schema
Time
Time key
Date
Event
Weekday flag
Weekend flag
Season
...
Product
Product key
Product number
Product name
Description
Size
Category name
Category descr.
Department name
Department descr.
...
Sales
Product fkey
Store fkey
Promotion fkey
Time fkey
Amount
Quantity
Store
Store key
Store number
Store name
Store address
Manager name
City name
City population
City area
State name
State population
State area
State major activity
...Promotion
Promotion key
Promotion descr.
Discount pct.
Type
Start date
End date
...

67. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 67
Logical DW Design: Snowflake Schemas
Time
Time key
Date
Event
Weekday flag
Weekend flag
Season
...
Category
Category key
Category name
Description
Department fkey
...
Department
Department key
Department name
Description
...
Product key
Product number
Product name
Description
Size
Category fkey
...
Sales
Product fkey
Store fkey
Promotion fkey
Time fkey
Amount
Quantity
Store
Store key
Store number
Store name
Store address
Manager name
City fkey
...
City
City key
City name
City population
City area
State fkey
...
State
State key
State name
State population
State area
State major activity
...
Promotion
Promotion key
Promotion descr.
Discount pct.
Type
Start date
End date
...
Product

68. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 68
Logical DW Design: Constellation Schemas
Purchases
Product fkey
Supplier fkey
Order time fkey
Due time fkey
Amount
Quantity
Freight cost
Time
Time key
Date
Event
Weekday flag
Weekend flag
Season
...
Product
Product key
Product number
Product name
Description
Size
Category name
Category descr.
Department name
Department descr.
...
Sales
Product fkey
Store fkey
Promotion fkey
Time fkey
Amount
Quantity
Store
Store key
Store number
Store name
Store address
Manager name
City name
City population
City area
State name
State population
State area
State major activity
...
Promotion
Promotion key
Promotion descr.
Discount pct.
Type
Start date
End date
...
Supplier
Supplier key
Supplier name
Contact person
Supplier address
City name
State name
...

69. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 69
Physical Data Warehouse Design: Views (1)

Data warehouses are several orders of magnitude larger
than operational database

Physical design is crucial for ensuring adequate
response time for complex queries

View: Table derived from a query

Query may involve base tables or other views

Materialized view : View physically stored in a database

Enhances query performance by precalculating costly operations

This is achieved at the expense of storage space

Updating materialized views: Modifications of underlying
base tables must be propagated into the view

Must be performed incrementally to ensure efficiency

70. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 70
Physical Data Warehouse Design: Views (2)

In a DW not all possible aggregations can be materialized

Number of aggregations grows exponentially with the number of
dimensions and hierarchies

Selection of materialized views: Identify the queries to be
prioritized, which determines the views to be materialized

DBMSs are providing tools that tune the selection of
materialized views given previous queries

Queries must be rewritten in order to best exploit existing
materialized views to improve response time

Drawback of materialized view approach: Requires one to
anticipate queries to be materialized

DW queries are often ad hoc and cannot always be anticipated

71. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 71
Physical Data Warehouse Design: Indexes (1)

Traditional indexes are not appropriate for data
warehouses

Designed for OLTP transactions that access a small number of
tuples

Bitmap indexes: For columns with a low number of
distinct values

Each value is associated with a bit vector whose length is the
number of records in the table

i
th
bit is set if i
th
row has that value in the column

Small in size, logical operators used to manipulate them
Professor
PId Name Rank
P1
P2
P3
P4
John Smith
Ada Jones
...
...
Assistant
Associate
Full
Associate
Bill Kock
Ed Low
...
...
Index on Rank
Rec#Assistant
1
Associate
2
3
4
1
Full
0 0
0 1 0
0 0 1
0 1 0
...

72. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 72
Physical Data Warehouse Design: Indexes (2)

Join indexes: Materialize relational join by keeping pairs
of row identifiers that participate in the join

Index selection: Select indexes to minimize execution
cost of a series of queries and updates

DBMSs are providing tools to assist DBAs to this task
P1
P1,C1,...
P1,C2,...
P2,C1,...
Index on
Client
Index on
Product
Sales
P3,C2,...
P2
P3
...
C1
C2
...
...

73. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 73
Fragmentation or Partitioning

Divide the contents of a relation into several files that can
be more efficiently processed

Vertical fragmentation: Splits attribute of a table into
groups that are stored independently

E.g., most often used attributes in one partition

Horizontal fragmentation: Divides records of a table into
groups according to a particular criterion

Range partitioning: Partitioned wrt range of values

E.g., partitioning based on time, each partition contains data about a
particular time period (year, month)

Hash partitioning: Partitioned wrt hash function

Combined partitioning: Combine these two methods

E.g., rows first partitioned according to range values, then subdivided
using a hash function

74. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 74
Data Warehouse Architecture (1)
Operational
databases
External
sources
Internal
sources
OLAP tools
Reporting
tools
Data mining
tools
Data marts
Back-end
tier
OLAP
tier
Front-end
tier
Data
sources
Data warehouse
tier
Statistical
tools
Data staging Metadata
ETL
process
Enterprise
data
warehouse
OLAP
server

75. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 75
Data Warehouse Architecture (2)

Data sources

Operational databases

Other internal or external sources of information (e.g. files)

Back-end tier

Extraction-Transformation-Loading (ETL) tools for manipulating
data from sources

Data staging area: Intermediate database where manipulation is
done

OLAP tier

OLAP Server: Supports multidimensional data and operations

Front-end tier: Deals with data analysis and visualization

Composed of OLAP tools, reporting tools, statistical tools, data-
mining tools, …

76. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 76
Back-End Tier

Extraction: Gathers data from multiple heterogeneous data
sources

May be operational databases or files in various formats

May be internal or external to the organization

Uses APIs such as ODBC, JDBC, … for achieving interoperability

Transformation: Modifies data to conform to the data
warehouse format

Cleaning: Removes errors, inconsistencies, format transformation

Integration: Reconciles data from different sources

Aggregation: Summarizes data according to the granularity (level of
detail) of the DW

Loading: Feeds the DW with transformed data

Also includes refreshing the data warehouse at a specified frequency

77. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 77
Data Warehouse Tier

Enterprise data warehouse: Centralized DW that
encompasses all areas in an organization

Data mart: Specialized DW targeted to a particular
functional area or user group

Their data can be derived from the enterprise DW or collected from
data sources

Metadata repository: Describes the content of the DW

Business metadata: Meaning (semantics) of data, organization
rules, policies, constraints, …

Technical metadata: How data is structured/stored in the computer

Data sources, data warehouse, and data marts: logical and physical
schemas, security information, monitoring information …

ETL process: Data lineage (trace to sources), rules, defaults, refresh and
purging rules, algorithms for summarization, …

78. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 78
OLAP Tier

OLAP servers that provides multidimensional view from
DWs and data marts

Can be ROLAP, MOLAP, or HOLAP

Most database products provide OLAP extensions and
related tools for manipulating cubes

However, no standardized language for querying data
cubes

Oracle uses Java and query language OLAP DML

SQL Server uses .NET and query language MDX

XMLA (XML for Analysis) aims at providing a common
language for exchanging multidimensional data

79. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 79
Front-End Tier

OLAP tools: Allow interactive exploration and
manipulation of the warehouse data

Facilitate formulation of ad hoc queries (no prior knowledge of
them)

Reporting tools: Enable production, delivery and
management of reports (paper and web-based)

Use predefined queries

Statistical tools: Used to analyze and visualize the cube
data using statistical methods

Data-mining tools: Allow users to analyze data to
discover patterns, trends, enable predictions

80. Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 80
Variations of the Architecture

Enterprise DW without data marts, or data marts without
enterprise DW

Building enterprise DW is costly, building a data mart is easier
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Integrating independently created data marts is costly
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No OLAP server: Client tools access directly DW
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Extreme situation: No DW either, client tools access sources
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Virtual DW: Set of materialized views over data sources
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Easier to build, but does not provide a real DW solution
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Does not contain historical data, no centralized metadata, no possibility
to clean and transform the data
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No data staging area: If the source systems conform very
closely to the data in the DW
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Rarely the case in real-world situations