Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 1 Chapter 5 Temporal Data Warehouses.
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Transcript of Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 1 Chapter 5 Temporal Data Warehouses.
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 1
Chapter 5
Temporal Data Warehouses
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 2
Fig. 5.1. Three different implementation types of slowly changing dimensions
(a) Type 1
(b) Type 2
(c) Type 3
Product
Size
101
102
Prod.number
...QB876
QD555
Name DescriptionSurr.key
...
...
Muesli
Olive Oil ...
...375
750
Product
Size
101
102
Prod.number
...QB876
QD555
Name DescriptionSurr.key
...
...
Muesli
Olive Oil ...
...375
750
Product
Current size
101
102
Prod.number
QB876
QD555
NameEffective
dateSurr.key
Muesli
Olive Oil
500
750
Original size
375
750
8/11/2006
7/10/2005
...
Description ...
......
...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 3
Fig. 5.2. Representing the temporal characteristics of real-world phenomena as events or as states
(a) Events
(b) States
1/09/04
Car accident
Time(day) ... 15/09/04 ......
1/04 4/04 ...
Project A
Time(month)
2/04 3/04 5/04 6/04 7/04 8/04 9/04 10/04
Project B
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 4
Fig. 5.3. Temporal data types
SimpleTime
ComplexTime
Instant
Time
Interval InstantSet
IntervalSet
(total,exclusive)
cs
(total,exclusive)
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 5
Fig. 5.4. Icons for the various synchronization relationships
meets
contains/inside
equals
starts
precedes
overlaps/intersects
covers/coveredBy
disjoint
finishes
succeeds
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 6
Fig. 5.5. A conceptual schema for a temporal data warehouse
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
VTSizeDistributor
LS
ResponsibleMax. amount
LS
VT
LS
District areaNo employees
LS
VT
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 7
Fig. 5.6. Types of temporal support for a level
(a) Temporal level
(b) Temporal level with temporal
attributes
(c) Non-temporal level with temporal
attributes
Product
Product numberNameDescriptionSizeDistributor
LS Product
Product numberNameDescription
SizeDistributor
VT
LS Product
Product numberNameDescription
SizeDistributor
VT
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 8
Fig. 5.7. A nontemporal relationship between temporal levels
Product
Product numberNameDescription
Pro
duct
gro
ups Category
Category nameDescription
VTResponsibleMax. amount
LS
SizeDistributor
LS
VT
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 9
Fig. 5.8. An example of an incorrect analysis scenario when a nontemporal relationship between temporal levels changes
(a) (b)
Timet1
PProduct
Product-Category
CCategory
t2
P-C
Timet1
P
C C1
t2
P-C1
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 10
Fig. 5.9. A temporal relationship between nontemporal levels
LS
Wor
k
Employee
Employee idEmployee namePosition...
Section
Section nameDescriptionActivity...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 11
Fig. 5.10. Links kept by a temporal relationship between nontemporal levels:
(a) before and (b) after deleting a section
(a) (b)
Timet1
EEmployee
Employee-Section
SSection
t2
E-S
Timet1
E
S1
t2
E-S1
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 12
Fig. 5.11. A temporal relationship between temporal levels
Store
Store numberNameAddressManager’s nameArea S
ales
org
ani
zatio
n Sales district
District nameRepresentativeContact info
LS
LS
District areaNo employees
LS
VT
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 13
Fig. 5.12. Instant and lifespan cardinalities between hierarchy levels
LS
Affi
liatio
n
Employee
Employee idEmployee namePosition...
Section
Section nameDescriptionActivity...LS
Wor
k LS
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 14
Fig. 5.13. Schema for analysis of indemnities paid by an insurance company
Policy No...
Indemnities
Client
Client No...
Insurance policy
Coverage
LS
LS
Risk No...
RiskLS
Amount VT
Repair No...
Repair work
LS Event
Event No...
LS
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 15
Fig. 5.14. Inclusion of loading time for measures
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
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 16Fig. 5.15. Inclusion of valid time for measures
(a) Events
(b) States
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
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 17
Fig. 5.16. Usefulness of including both valid time and loading time
100
LT1
10 no sales
10 13 ...Time
(weeks) 11
5200 500
2012 14
LT2
Sales
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 18
Fig. 5.17. A temporal data warehouse schema for an insurance company
Insurance type
Type idInsurance nameCategory...
Insurance object
Object idObject name ...
Insurance agency
Agency idAddress...
Frauddetection
AmountTT
Client
Client idClient nameAddress...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 19
Fig. 5.18. Usefulness of valid time, transaction time, and loading time
100 VT[2:5]
LT1
1 4 ...
Salary
Time(months) 2 83
LT2
200 VT[6:now]
TT1 TT2
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 20
Fig. 5.19. An example of distribution of measures in the case of temporal relationships
(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
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 21
Fig. 5.20. Different temporal granularities in dimensions and measures
6 8 3Quantity sold
Time (week)
Store-Sales district
...
4 5 1
Time (month) Jan
S-SD S-SD1
Feb
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 22
Fig. 5.21. Example of a coercion function for salary
20
1 3 6
1 2
Sources (month)
Salary
Data warehouse(quarter)
9
VT1
3
30 40
VT2 VT3
20Average salary 30 ?
LT
2
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 23
Fig. 5.22. Metamodel of the temporally extended MultiDim model
/Name: string/Temporal: Boolean
Dimension
Criterion: string/Temporal: Boolean
Hierarchy
1..*
DimHierAgg
1
HierLevAgg
1..*
2..*
1..*
Name: stringTemSup: Temp [0..n]
Level
1..*
0..*
child parent
1
0..*
Additivity: AddType
Measure
LevAttrAgg
KeyAttrAgg
1
1..*
Name: stringSync: SyncRel
Fact relationship
1
0..*2..*
1..*MeasAgg
Generalization
AggregationComposition
Association
Derived attribute/
Identified0..*
Key
Connects
RoleName: string
Related
Name: stringType: DataTypeDerived: BooleanTempSup: Temp [0..n]
Attribute
xor
1
1
« enumeration »DataType
integerrealstringTDType...
« enumeration »TDType
InstantIntervalInstantSet...
« enumeration »SyncRel
meetsoverlapscontains...
« enumeration »AddType
additivesemiadditivenonadditive
MinInstChildCard: intMaxInstChildCard: intMinInstParentCard: intMaxInstParentCard: intMinLifespChildCard: intMaxLifespChildCard: intMinLifespParentCard: intMaxLifespParentCard: intDistrFactor: BooleanTempSup: Temp [0..n]Sync: SyncRel
« data type »Temp
Type: TempTypeDataType: TDTypeGran: Granularity
« enumeration »TempType
LSVTTTLT
« enumeration »Granularity
secminhour...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 24
Fig. 5.23. Mapping levels with temporal attributes
(a) ER schema (b) Object-relational representation
(c) ER schema (d) Object-relational representation
Product
Product numberNameDescriptionSize (1,n) Value VT FromDate ToDate ...
Product
FromDate ToDate
VT
Size*
ValuePId
1
2
Productnumber
QB876
QD555
...
...
... ...
...
...10
20
18
05/2002
09/2002
05/2002
08/2002
07/2003
now
Product
FromDate ToDate
VT*
Size*
ValuePId
1
2
Productnumber
QB876
QD555
...
...
... ...
...
...10
20
18
05/2002
09/2002
08/2003
05/2002
08/2002
07/2003
now
now
Product
Product numberNameDescriptionSize (1,n) Value VT (1,n) FromDate ToDate...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 25
Fig. 5.24. Mapping a temporal level
(a) ER schema (b) Object-relational representation
Product
LS (1,n)FromDate
ToDateProduct number NameDescriptionSize (1,n) Value VT (1,n) FromDate ToDate ...
Product
10
LS*
FromDate ToDate
VT*
Size*
ValuePId
1
2
3
Productnumber
...
...
...
...
QB876
QD555
QE666
20
15
18
25
18
05/2002 08/2002
07/200309/2002
07/2003 now
now05/2002
05/2002 08/2003
now09/2004
FromDate ToDate
now
now
08/2003
now
05/2002
09/2004
05/2002
05/2002
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 26
Fig. 5.25. Mapping a hierarchy with a nontemporal relationship
(a) ER schema
(b) Object-relational representation
CategoryProduct (1,n)(1,1)
Product numberNameDescriptionSize (1,n) Value VT (1,n) FromDate ToDate
Category nameDescriptionResponsible (1,n) Name VT (1,n) FromDate ToDate
ProdCat
Product
10
VT*
Size*
ValuePId
Productnumber
...
...
...
1
2
QB876
QD555
CategoryRef
C1
C218
20
FromDate
08/200205/2002
08/2003
09/2002
05/2002
ToDate
now
07/2003
now
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 27
Fig. 5.26. Various cardinalities for temporal relationships linking nontemporal levels
(a)
(c)
(b)
LS
Wor
k
Employee
Employee idEmployee namePosition...
Section
Section nameDescriptionActivity...
Wor
k
Employee
Employee idEmployee namePosition...
Section
Section nameDescriptionActivity...
LS
LS
Employee
Employee idEmployee namePosition...
Section
Section nameDescriptionActivity...
Wor
k LS
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 28
Fig. 5.27. Mapping the schema given in Fig. 5.26a into the ER model
SectionEmployee (1,1) (1,n)
LS (1,n) FromDate ToDate
Employee idEmployee namePosition...
Section nameDescriptionActivity...
Works
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 29
Fig. 5.28. Various object-relational representations of temporal links
(a) One-to-many cardinalities
(b) One-to-many cardinalities
(c) Many-to-many cardinalities
Works
S1
S1
LS*SectionRef
Empl.Ref
E1
E2 05/2002
FromDate ToDate
now
now
08/2002
07/2003
05/2002
WId
1
2
Employee
05/2002
S1
S1
FromDate ToDate
LS*
InSection
SectionRef
EId
1
2
Empl.id
E2244
E2345 ...
...
...
now
now
08/2002
07/2003
05/2002
Employee
05/2002
S1
S2
S1
FromDate ToDate
LS*
InSection*
SectionRef
EId
1
2
Empl.id
E2244
E2345 ...
...
...
now
now
06/2003
08/2002
07/2003
05/2002
09/2002
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 30
Fig. 5.29. Mapping a temporal relationship between temporal levels
(a) MultiDim schema
(b) Object-relational representation
Store
Store numberNameAddressManager’s nameArea S
ales
org
ani
zatio
n Sales district
RepresentativeContact info
LS
LS
LS
VTDistrict nameDistrict areaNo employees
LS
SalesDistrict
LS*DId
A
B
District name
Ixelles
Forest
...
...
...
FromDate ToDate
05/2002 08/2003
10/2004 now
now05/2002
Store
A
B
B
B
LS*
SalesOrganization*
DistrictRef
LS*SId
1
2
3
Storenumber
QB876
QE666
QD555
...
...
...
...
FromDate ToDate
05/2002
10/2004
09/2002
05/2002
05/2002 now
now
09/2004
now
08/2002
FromDate ToDate
now
now
now
05/2002
05/2002
05/2002
09/2004
08/2003
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 31
Fig. 5.30. Mapping of the fact relationship shown in Fig. 5.5
(a) ER representation
(c) Object-relational representation
(b) Relational table for the Quantity measure
Quantity (1,n) Value VT (1,n)Amount (1,n) Value VT (1,n)
Sales
Sales
Client fkey
Product fkey
Storefkey Quantity VT
C1
C1
C1
C1
...
C1
P1
P1
P1
P1
...
P1
... ... ...
S2
S2
S2
S1
S1 100
100
200
50
80
05/2002
06/2002
07/2002
07/2002
05/2002C1 P1 S1
150 06/2002
Sales
10000 05/2002
15000
18000
06/2002
07/2002
10005/2002
150
07/2002
06/2002
2000005/2002
5000
07/2002
06/2002
200 05/2002
80
06/2002
07/2002
50
...
VT*
Quantity*
Value VT*
Amount*
Value
ClientRef
C1
ProductRef
StoreRef
C1
...
P1 S1
P1 S2
... ... ... ... ...
SId
1
2
...
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 32
Fig. 5.31. Approaches to measure aggregation in the presence of temporal relationships
(a) Excerpt from the schema shown in Fig. 5.5
Store
Store numberNameAddressManager’s nameArea
Sales district
District nameRepresentativeContact info
Sales
Quantity Amount VT
LS
LS
LSDistrict areaNo employees
LS
VT
Sal
es o
rga
niza
tion
Copyright © 2008 Elzbieta Malinowski & Esteban Zimányi 33
Fig. 5.31. Approaches to measure aggregation in the presence of temporal relationships
(b) Tables for the parent-child and fact relationships
(e) Eder and Koncilia’s aggregation
(c) Sales districts and the measure Quantity for stores S1, S2, and S3
(d) Traditional temporal aggregation
StoreRef Quantity Amount FromDate ToDate
StoreRef SDRef FromDate ToDate
Sales
Store_SD
5 8 4 6 4 3S1
SD SD1
S2 2 4 5 4 8 7
SD SD1
S3 5 4 3 7 6 9
SD SD2
SD2 7 6 9
SD 12 16 12
SD1 10 12 10
SD2 5 4 3 7 6 9
SD1 7 12 9 10 12 10