1. CAiSE’13CAiSE 13
Business Model Ontologies
in OLAP Cubes
Christoph Schütz, Bernd Neumayr, Michael Schreflp , y ,
This work was supported by the FIT-IT research program of the Austrian Federal Ministry for Transport,
Innovation, and Technology under grant FFG-829594 for the Semantic Cockpit project.

2. CAiSE’13
Overview
CAiSE 13
 Introduction
■ Facts with Ontology-valued Measures
□ Base Facts
□ Shared Facts
■ OLAP with Ontology-valued Measures
M□ Merge
□ Abstraction
Implementation■ Implementation
■ Summary and Future Work
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3. CAiSE’13
Introduction
CAiSE 13
■ Traditional cube: Numeric measures
■ Many real-world facts do not boil down to numeric values
■ How do you measure complex situations?
Example: intensity of competition
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4. CAiSE’13
Introduction
CAiSE 13
■ Analysts compile strategic analysis documents
■ Not (only) numeric measures■ Not (only) numeric measures
 Ontology-valued measures
x:Marketing/Germany/Q1-
2012
x:Marketing/France/Q1-2012
x:Germany/Sales/Q2-2012
Germany
x:Germany/Production/Q2-2012
x:MegaCar x:sells x:MegaSUV
x:Familiesx:hasClient
x:We
x:Our_Truck
x:produces
x:Development/Germany/
Q1-2012
x:Development/France/Q1-
2012
x:France/Sales/Q2-2012 x:France/Production/Q2-2012
x:We x:sells x:Our_Truck
x:Food_Incx:hasClient x:MegaCar
x:hasSupplier
x:France/Sales/Q2 2012 x:France/Production/Q2 2012
Q1-2012France
x:MegaCar x:sells x:MegaSUV
x:Singlesx:hasClient
W ll O r SUV
x:MegaCar
x:produces
x:MegaSUV
x:MidiCarx:hasSupplier
x:hasSupplier
4JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge EngineeringSales Production
Q2-2012
x:We x:sells x:Our_SUV
x:Familiesx:hasClient
x:We
x:Our_SUV
x:produces
asSupp e

5. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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6. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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7. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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8. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
8JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

9. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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10. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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11. CAiSE’13
Introduction
CAiSE 13
■ Roll-up along the dimension hierarchies
Combine knowledge from different contexts■ Combine knowledge from different contexts
U
nion
Intersection
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12. CAiSE’13
Introduction
CAiSE 13
■ Alter the granularity of the ontologies
Use knowledge from the ontologies for■ Use knowledge from the ontologies for
abstraction
x:Europe/Sales/Q2-2012 x:Europe/Sales/Q2-2012
x:MegaCar x:sells x:SUVsx:MegaCar x:sells x:MegaSUV
h Cli
Europe Abstract Europe
x:Singlesx:hasClient
x:Familiesx:hasClient
x:hasClient x:Food_Inc
x:Households
x:Corporate
x:hasClient
x:hasClient
x:sells
Q2-2012
x:We
x:Our_Truck
x:sells
x:Our_SUV
x:sells
x:hasClient
x:Trucksx:We
x:hasClient
x:sells
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Sales
Q2-2012
Sales
x:sells

13. CAiSE’13
Introduction
CAiSE 13
■ Alter the granularity of the ontologies
Use knowledge from the ontologies for■ Use knowledge from the ontologies for
abstraction
x:Europe/Sales/Q2-2012 x:Europe/Sales/Q2-2012
x:MegaCar x:sells x:SUVsx:MegaCar x:sells x:MegaSUV
h Cli
Europe Abstract Europe
x:Singlesx:hasClient
x:Familiesx:hasClient
x:hasClient x:Food_Inc
x:Households
x:Corporate
x:hasClient
x:hasClient
x:sells
Q2-2012
x:We
x:Our_Truck
x:sells
x:Our_SUV
x:sells
x:hasClient
x:Trucksx:We
x:hasClient
x:sells
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Sales
Q2-2012
Sales
x:sells

14. CAiSE’13
Introduction
CAiSE 13
■ Alter the granularity of the ontologies
Use knowledge from the ontologies for■ Use knowledge from the ontologies for
abstraction
x:Europe/Sales/Q2-2012 x:Europe/Sales/Q2-2012
x:MegaCar x:sells x:SUVsx:MegaCar x:sells x:MegaSUV
h Cli
Europe Abstract Europe
x:Singlesx:hasClient
x:Familiesx:hasClient
x:hasClient x:Food_Inc
x:Households
x:Corporate
x:hasClient
x:hasClient
x:sells
Q2-2012
x:We
x:Our_Truck
x:sells
x:Our_SUV
x:sells
x:hasClient
x:Trucksx:We
x:hasClient
x:sells
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Sales
Q2-2012
Sales
x:sells

15. CAiSE’13
Overview
CAiSE 13
■ Introduction
 Facts with Ontology-valued Measures
□ Base Facts
□ Shared Facts
■ OLAP with Ontology-valued Measures
M□ Merge
□ Abstraction
Implementation■ Implementation
■ Summary and Future Work
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16. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
Agents
ResourcesResources
Events
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17. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
Agents
Resources
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
x:OurTruck
x:Money
Resources
x:Germany_Q2 2012_
Payment_OurTruck
Events
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18. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:provide
Agents
Resources
x:qtySold
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruck
rea:stockflow
x:Moneyrea:stockflow
Resources
x:Germany_Q2 2012_
Payment_OurTruckrea:provide
x:revenue
Events
10,200,000
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19. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:type
rea:provide
Agents
Resourcex:qtySold
rdf:type
x:ProductModel
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruck
rea:stockflow
x:Moneyrea:stockflow
Types x:PaymentType
rdf:type
x:Germany_Q2 2012_
Payment_OurTruckrea:provide
x:revenue
Event Groups
rea:Group
rdf:type 10,200,000
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20. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:type
rea:provide
Agents
Resourcex:qtySold
rdf:type
x:ProductModel
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruck
rea:stockflow
x:Moneyrea:stockflow
Types x:PaymentType
rdf:type
x:Germany_Q2 2012_
Payment_OurTruckrea:provide
x:revenue
Event Groups
rea:Group
rdf:type 10,200,000
A li ti M d l
x:Salerea:Event
rdfs:subClassOf
Application Model
rea:Agent
Metamodel Metamodel
x:Sale
x:exchange
rdfs:domain
rdfs:range
rea:Event
rdfs:subClassOf
rea:provide rea:receive
rdfs:domain
rdfs:range
rdfs:domain
rdfs:range
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x:Payment
rdfs:range
rea:Event
rdfs:range rdfs:range

21. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:type
rea:provide
Agents
Resource
x:Sale
rdf:type
x:qtySold
rdf:type
x:ProductModel
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruck
rea:stockflow
x:Moneyrea:stockflow
Types
rdf:type
x:PaymentType
rdf:type
x:Germany_Q2 2012_
Payment_OurTruckrea:provide
x:revenue
Event Groups
rea:Group
rdf:type
rea:Resource
rdf:type
rdf:type
rea:Agent
rdf:type
rdf:type
x:Payment
rdf:type
10,200,000
A li ti M d l
x:Salerea:Event
rdfs:subClassOf
Application Model
rea:Agent
Metamodel Metamodel
x:Sale
x:exchange
rdfs:domain
rdfs:range
rea:Event
rdfs:subClassOf
rea:provide rea:receive
rdfs:domain
rdfs:range
rdfs:domain
rdfs:range
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x:Payment
rdfs:range
rea:Event
rdfs:range rdfs:range

22. CAiSE’13
Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:type
rea:provide
Agents
Resource
x:Sale
rdf:type
x:qtySold
rdf:type
x:ProductModel
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruck
rea:stockflow
x:Moneyrea:stockflow
Types
rdf:type
x:PaymentType
rdf:type
x:Germany_Q2 2012_
Payment_OurTruckrea:provide
x:revenue
Event Groups
rea:Group
rdf:type
rea:Resource
rdf:type
rdf:type
rea:Agent
rdf:type
rdf:type
x:Payment
rdf:type
10,200,000
Resource
Groups
Agents
x:Germany_Q2-2012_
Sales_FunnySUVs
x:FunnyCar
x:SUVs
rea:stockflow
rdf:type
rdf:type
rdf:type
rea:provide
rea:receive
rdf:type
rdf:type
x:Sale
rdf:type
Resource
Types
g
x:exchange
x:Germany_Q2-2012_
Payment_FunnySUVsx:Families
x:Money
rea:stockflowAgent Groups
Event Groupsrdf:type
rea:provide
rea:receive
rdf:type
rdf:type
rea:Grouprdf:type
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Event Groups
rea:Group
rdf:type
rea:Agent
rdf:type
x:Paymentrea:Group
rdf:type
x:PaymentType

23. CAiSE’13
Base Facts
CAiSE 13
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24. CAiSE’13
Shared Facts
CAiSE 13
■ Shared facts represent asserted knowledge at more abstract
l l f b t tilevels of abstraction
■ Base facts inherit knowledge represented in the more abstract
shared factsshared facts
■ Shared facts facilitate the analysis
‹ all › ‹ all ›
‹ all ›Location
Organization
Time
‹ continent ›
‹ country ›
‹ year ›
‹ quarter ›
‹ department ›
Strategy
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+ competition: RDF
Strategy

25. CAiSE’13
Shared Facts
CAiSE 13
x:Organization Model
Time: ‹ all ›
: Strategy
Organization: ‹ all ›
x:Organization_Model
Location: ‹ all ›
x:OurTruck
x:ProductModel
x:Enterprise
rea:Agent
rdfs:subClassOf
rdf:type
rdf:type
+ competition =
x:Organization_Model
S l M d l
rdf:type
x:We
x:OurTruckx:Enterprise
x:FunnyCar x:CleverCar
rdf:type rdf:type x:OurSUV
Sales: ‹ department ›
Organization: ‹ all ›
x:Sales_Model
x:Families x:Singles
x:Households
x:OurTruck x:OurSUV
x:SUVsx:Trucks
rea:grouping
rea:grouping
rea:groupingrea:grouping
Time: ‹ all ›
+ competition =
x:Sales_Model
: Strategy
Location: ‹ all ›
x:Families
rea:Group
x:Singles
rdf:typerdf:type
x:Enterprise
x:Food_Inc
rdf:type
x:PaymentType
x:Money
rdf:type
Production: ‹ department ›
Organization: ‹ all ›
x:Production_Model
x:ProductModel
rdf:type
x:ToolModel
rdf:type
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Location: ‹ all › Time: ‹ all ›
+ competition =
x:Production_Model
: Strategy
x:Enterprise x:Binford
rdf:type
x:OurTruckEngine
x:CleverCarChassis
x:FunnySUVEngine
rdf:type
rdf:type
x:BinfordRobot

26. CAiSE’13
Shared Facts
CAiSE 13
x:Organization Model
Time: ‹ all ›
: Strategy
Organization: ‹ all ›
x:Organization_Model
Location: ‹ all ›
x:OurTruck
x:ProductModel
x:Enterprise
rea:Agent
rdfs:subClassOf
rdf:type
rdf:type
+ metamodel
and
common
+ competition =
x:Organization_Model
S l M d l
rdf:type
x:We
x:OurTruckx:Enterprise
x:FunnyCar x:CleverCar
rdf:type rdf:type x:OurSUV
application
model
Sales: ‹ department ›
Organization: ‹ all ›
x:Sales_Model
x:Families x:Singles
x:Households
x:OurTruck x:OurSUV
x:SUVsx:Trucks
rea:grouping
rea:grouping
rea:groupingrea:grouping
+ salesTime: ‹ all ›
+ competition =
x:Sales_Model
: Strategy
Location: ‹ all ›
x:Families
rea:Group
x:Singles
rdf:typerdf:type
x:Enterprise
x:Food_Inc
rdf:type
x:PaymentType
x:Money
rdf:type
+ sales
application
model
Production: ‹ department ›
Organization: ‹ all ›
x:Production_Model
x:ProductModel
rdf:type
x:ToolModel
rdf:type
+ production
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Location: ‹ all › Time: ‹ all ›
+ competition =
x:Production_Model
: Strategy
x:Enterprise x:Binford
rdf:type
x:OurTruckEngine
x:CleverCarChassis
x:FunnySUVEngine
rdf:type
rdf:type
x:BinfordRobot
+ production
application
model

27. CAiSE’13
Shared Facts (Inheritance)
CAiSE 13
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28. CAiSE’13
Overview
CAiSE 13
■ Introduction
■ Facts with Ontology-valued Measures
□ Base Facts
□ Shared Facts
 OLAP with Ontology-valued Measures
M□ Merge
□ Abstraction
Implementation■ Implementation
■ Summary and Future Work
28JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

29. CAiSE’13
Merge (Union)
CAiSE 13
x:Germany_Sales_Q2-2012
x:Germany_Q2-2012_
Sales_OurTruck
x:We
rea:provide
x:Food_Inc
rea:receive x:OurTruck
rea:stockflow
x:France_Sales_Q2-2012
x:France_Q2-2012_
Sales_OurSUV
x:We
rea:provide
x:Families
rea:receive x:OurSUV
rea:stockflow
rea:receive x:OurTruck
x:Germany_Q2-2012_
Sales_FunnySUVs
x:FunnyCar
rea:provide
x:Families
i
x:SUVs
rea:stockflow
rea:receive
x:France_Q2-2012_
Sales_FunnySUVs
x:FunnyCar
rea:provide
x:Singles
i
x:SUVs
rea:stockflow
x:Families
rea:receive
x:SUVs x:Singles
rea:receive
x:SUVs
x:Europe_Sales_Q2-2012
x:Germany Q2-2012x:Food Inc
rea:receive rea:stockflow
Union
CONSTRUCT { ?s ?p ?o } WHERE {
{
GRAPH x:Germany Sales Q2 2012 {
x:Germany_Q2-2012_
Sales_OurTruck
_
x:We
rea:provide
x:France_Q2-2012_
Sales_OurSUV
x:Families
rea:receive
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
rea:stockflow
Union
GRAPH x:Germany_Sales_Q2-2012 {
?s ?p ?o
} UNION
GRAPH x:France_Sales_Q2-2012 {
?s ?p ?o
}
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:France_Q2-2012_
Sales FunnySUVs
kfl
rea:provide
x:SUVs
rea:stockflow
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}
}
Sales_FunnySUVs
x:Singles
rea:receive
rea:stockflow

30. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
x:France_Q2-2012_
Sales_FunnySUVs
x:Singles
rea:receive
rea:stockflow
rea:provide
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31. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
x:France_Q2-2012_
Sales_FunnySUVs
x:Singles
rea:receive
rea:stockflow
rea:provide
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32. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
Sales Groups
x:France_Q2-2012_
Sales_FunnySUVs
x:Singles
rea:receive
rea:stockflow
rea:provide
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33. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
Sales Groups
x:France_Q2-2012_
Sales_FunnySUVs
x:Singles
rea:receive
rea:stockflow
rea:provide
 RDFS Reasoner
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34. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive
x:OurTruck
rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
x:Sale
rdf:type
rdf:type
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
rea:stockflow
x:SUVs
rea:stockflow
x:Sale
rdf:type
Sales Groups
x:France_Q2-2012_
Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Sale
rdf:type
 RDFS Reasoner
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35. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive
x:OurTruck
rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
x:Sale
rdf:type
rdf:type
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
rea:stockflow
x:SUVs
rea:stockflow
x:Sale
rdf:type
Sales Groups
x:France_Q2-2012_
Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Sale
rdf:type
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36. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d I
rea:receive
x:OurTruck
rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
x:Sale
rdf:type
rdf:type
Sa es_Ou SU
x:Families
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
rea:stockflow
x:SUVs
rea:stockflow
x:Sale
rdf:type
Sales Groups
x:France_Q2-2012_
Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Sale
rdf:typex:Households
rea:grouping
rea:grouping
x:Families x:Singles
g p g
rdf:typerdf:type
36JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
rea:Group

37. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012_Abstraction
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
_
x:Households
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
x:France_Q2-2012_
Sales_FunnySUVs
rea:stockflow
rea:provide
rea:receive
37JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

38. CAiSE’13
Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012_Abstraction
F d I
rea:receive rea:stockflow
x:Germany_Q2-2012_
Sales_OurTruck
x:Food_Inc
x:We
rea:provide
x:France_Q2-2012_
Sales OurSUV
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
_
x:Households
rea:receive
x:Germany_Q2-2012_
Sales_FunnySUVs
rea:receive
x:FunnyCar
rea:provide
x:SUVs
rea:stockflow
x:France_Q2-2012_
Sales_FunnySUVs
rea:stockflow
rea:provide
rea:receive
38JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

39. CAiSE’13
Merge (Intersection)
CAiSE 13
39JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

40. CAiSE’13
Merge (Intersection)
CAiSE 13
40JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

41. CAiSE’13
Overview
CAiSE 13
■ Introduction
■ Facts with Ontology-valued Measures
□ Base Facts
□ Shared Facts
■ OLAP with Ontology-valued Measures
M□ Merge
□ Abstraction
 Implementation Implementation
■ Summary and Future Work
41JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

42. CAiSE’13
Implementation
CAiSE 13
■ Based on hetero-homogeneous data warehouse
htt //hh d dk i li t/http://hh-dw.dke.uni-linz.ac.at/
O l DB f th ltidi i l d l■ Oracle DB for the multidimensional model
■ Jena tuple store for RDF graphs and Jena framework for
SPARQL queriesSPARQL queries
U i th ltidi i l d l i O l DB i d f■ Using the multidimensional model in Oracle DB as index for
calculating the inherited knowledge
42JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

43. CAiSE’13
Overview
CAiSE 13
■ Introduction
■ Facts with Ontology-valued Measures
□ Base Facts
□ Shared Facts
■ OLAP with Ontology-valued Measures
M□ Merge
□ Abstraction
Implementation■ Implementation
 Summary and Future Work
43JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering

44. CAiSE’13
Summary and Future Work
CAiSE 13
■ Ontology-valued measures for complex real-world facts that do
t b il d t inot boil down to a numeric measure
Oth b i d l t l i f t l l d■ Other business model ontologies for ontology-valued measures
In particular: e3value and its variants, e.g., e3forces
■ Provide for easier querying, examine other query languages
44JKU Linz  Institut für Wirtschaftsinformatik – Data & Knowledge Engineering