This document discusses key concepts in dimensional modeling including measures, dimensions, hierarchies, levels, and facts. It defines measures as numeric values used to evaluate aspects of an organization. Dimensions are abstract concepts that group related data and are composed of hierarchies and levels. Levels describe real-world concepts with similar characteristics, such as products or categories. Facts express the focus of analysis as an n-ary relationship between levels and can contain measures, which are numerical data analyzed using dimension perspectives. The document also categorizes measures as additive, semi-additive, or non-additive and discusses supporting temporal data.

2. INTRODUCTION
TYPES &
CASES
OBJECTIVES

3.  Measures are usually numeric values
that are used for quantitative
evaluation of aspects of an
organization
 For example, measures such as the
amount or quantity of sales might
help to analyze sales activities in
various stores.

4. Product
Product number
Name
Description
Product groups
Category
Category name
Description
LS
Store
Store number
Name
Address
Manager’s name
Area
Sales organization
Sales district
District name
Representative
Contact info
Client
Client id
First name
Last name
Birth date
Profession
Salary range
Address
Sales
Quantity
AmountVT
LS
LS
VT
Size
Distributor
LS
Responsible
Max. amount
LS
VT
LS
District area
No employees
LS
VT

5.  A dimension is an abstract concept
that groups data that shares a
common semantic meaning within the
domain being modeled.
 A dimension is composed of a set of
hierarchies and a hierarchy is in turn
composed of a set of levels.

6.  A level corresponds to an entity type
in the ER model. It describes a set of
real-world concepts that, from the
application’s perspective, have
similar characteristics.
 For example, Product, Category, and
Department are some of the levels
Instances of a level are called
members.

7. Level name
Key attributes
Other attributes
Child level
name
Key attributes
Other attributes
Parent level
name
Key attributes
Other attributes
Level
Hierarchy

8.  A level has a set of attributes that
describe the characteristics of their
members.
 In addition, a level has one or several
keys that identify uniquely the
members of a level, each key being
composed of one or several
attributes.

9.  A fact relationship expresses a focus
of analysis and represents an n-ary
relationship between levels.
 A fact relationship may contain
attributes commonly called
measures.
 These contain data (usually
numerical) that is analyzed using the
various perspectives represented by
the dimensions.

10.  We classified measures as
 additive,
 semiadditive,
 nonadditive.

11.  Temporal Measure can be either
• Support for Non-aggregated
Measure
• Support for Aggregated Measure

12. Non-Aggregated
Measures

13. 1. Sources Non temporal, Data
Warehouse with LT
2. Sources and Data Warehouse with VT
3. Sources with TT, Data Warehouse with
VT
4. Sources with VT, Data Warehouse
with VT and LT
5. Sources with TT, Data Warehouse with
TT (and optionally LT and VT)
6. Sources with BT, Data Warehouse
with BT and LT

14. Sources Nontemporal,
Data Warehouse with LT
CASE 1

15. Category
Category name
Description
...
Product
Product number
Product name
Description
Size
...
Productgroups
Supplier
Supplier id
Supplier name
Adress
...
Warehouse
WH number
WH name
Address
City name
State name
...
Inventory
Quantity
Cost
LT
Inclusion of loading time for measures

16. Sources and Data Warehouse
with VT
CASE 2

17. Transaction type
Id
Name
...
Account
Account id
Account type
...
Transactions
AmountVT
Client
Client id
Client name
Address
...
Project
Project id
Project name
Objectives
Size
...
Employee
Employee id
Employee name
Address
...
Department
Department id
Department name
Manager
...
Works
SalaryVT
Inclusion of valid time for measures (Event, States)

18. Sources with TT, Data
Warehouse with VT
CASE 3

19. Project
Project id
Project name
Objectives
Size
...
Employee
Employee id
Employee name
Address
...
Department
Department id
Department name
Manager
...
Works
SalaryVT
Inclusion of valid time for measures

20. Sources with VT, Data
Warehouse with VT
CASE 4

21. 100
LT1
10 no sales
10 13 ...
Time
(weeks) 11
5200 500
2012 14
LT2
Sales
Usefulness of including both
valid time and loading time

22. Sources with TT, Data
Warehouse with TT (and
optionally LT and VT)
CASE 5

23. Insurance type
Type id
Insurance name
Category
...
Insurance object
Object id
Object name
...
Insurance agency
Agency id
Address
...
Fraud
detection
AmountTT
Client
Client id
Client name
Address
...
A temporal data warehouse schema
for an insurance company

24. Sources with BT, Data
Warehouse with BT and LT
CASE 6

25. 100 VT[2:5]
LT1
1 4 ...
Salary
Time
(months) 2 83
LT2
200 VT[6:now]
TT1 TT2
Usefulness of valid time, transaction time,
and loading time

26. Aggregated
Measures

27. (a) (c)(b)
SD2SD1
2535
1020 30
Time
SD1 SD2
Sales district of store S
Measure for store S
Measure distributed
between sales districts
SD2SD1
3020
3020
Time
SD1 SD2
SD2SD1
3614
3020
Time
SD2SD1
An example of distribution of measures in the case of
temporal relationships
Store
Store number
Name
Address
Manager’s name
Area
Salesorganization
Sales district
District name
Representative
Contact info
LS
LS
District area
No employees
LS
VT

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