dwm temporal measure

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

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Thank You