dwm temporal measure
DESCRIPTION
What is Temporal Measure in Temporal Data WarehouseTRANSCRIPT
Temporal Data warehouse
Temporal Measures
Vijay Kumar Verm
a (VJY)
11-April-2014
INTRODUCTION
TYPES & CASES
OBJECTIVES
What is M
easures 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.
Product
Product numberNameDescription
Product groups
Category
Category nameDescription
LS
Store
Store numberNameAddressManager’s nameArea
Sales organization
Sales district
District nameRepresentativeContact info
Client
Client idFirst nameLast nameBirth dateProfessionSalary rangeAddress
Sales
Quantity Amount VT
LS
LS
VT SizeDistributor
LS
ResponsibleMax. amount
LS
VT
LS
District areaNo employees
LS
VT
What is dim
ension 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.
What is Levels 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.
Level name
Key attributes Other attributes
Child level name
Key attributesOther attributes
Parent level name
Key attributes Other attributes
Level
Hierarchy
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.
Fact Relatio
nship 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.
We classified measures as additive, semiadditive, nonadditive.
TYPES OF MEASURES Temporal Measure can be
either• Support for Non-aggregated
Measure• Support for Aggregated
Measure
Non-Aggregated Measures
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
Sources Nontemporal, Data Warehouse with LT
CASE 1
Category
Category nameDescription...
Product
Product numberProduct nameDescriptionSize... P
rodu
ct g
roup
s
Supplier
Supplier idSupplier nameAdress ...
Warehouse
WH numberWH nameAddressCity nameState name...
Inventory
Quantity CostLT
Inclusion of loading time for measures
Sources and Data Warehouse
with VT
CASE 2
Transaction type
IdName ...
Account
Account idAccount type...
Transactions
AmountVT
Client
Client idClient nameAddress...
Project
Project idProject nameObjectivesSize...
Employee
Employee idEmployee name Address...
Department
Department idDepartment name Manager...
Works
SalaryVT
Inclusion of valid time for measures (Event, States)
Sources with TT, Data Warehouse with VT
CASE 3
Project
Project idProject nameObjectivesSize...
Employee
Employee idEmployee name Address...
Department
Department idDepartment name Manager...
Works
SalaryVT
Inclusion of valid time for measures
Sources with VT, Data Warehouse with VT
CASE 4
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
Sources with TT, Data Warehouse with TT (and
optionally LT and VT)
CASE 5
Insurance type
Type idInsurance nameCategory...
Insurance object
Object idObject name ...
Insurance agency
Agency idAddress...
Frauddetection
AmountTT
Client
Client idClient nameAddress...
A temporal data warehouse schema for an insurance company
Sources with BT, Data Warehouse with BT and LT
CASE 6
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
Aggregated Measures
(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 numberNameAddressManager’s nameArea S
ales
org
ani
zatio
n Sales district
District nameRepresentativeContact info
LS
LS
District areaNo employees
LS
VT
Thank You