Download - Christoph scuetz caise bmo-olap_2013
CAiSE’13CAiSE 13
Business Model Ontologiesin 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.
CAiSE’13Overview
CAiSE 13
Introduction■ Facts with Ontology-valued Measures
□ Base Facts□ Shared Facts
■ OLAP with Ontology-valued MeasuresM□ Merge
□ Abstraction
Implementation■ Implementation■ Summary and Future Work
2JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
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
3JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
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/Q1-2012x:Germany/Sales/Q2-2012
Germany
x:Germany/Production/Q2-2012
x:MegaCar x:sells x:MegaSUV
x:Familiesx:hasClient
x:Wex: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:MegaCarx:produces
x:MegaSUV
x:MidiCarx:hasSupplierx:hasSupplier
4JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge EngineeringSales Production
Q2-2012x:We x:sells x:Our_SUV
x:Familiesx:hasClientx:We
x:Our_SUVx:produces
asSupp e
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
5JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
6JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
7JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
8JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
9JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
10JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Roll-up along the dimension hierarchiesCombine knowledge from different contexts■ Combine knowledge from different contexts
Union
Intersection
11JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Introduction
CAiSE 13
■ Alter the granularity of the ontologiesUse 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:sellsx:hasClient
x:Trucksx:We
x:hasClient
x:sells
12JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
SalesQ2-2012
Sales
x:sells
CAiSE’13Introduction
CAiSE 13
■ Alter the granularity of the ontologiesUse 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:sellsx:hasClient
x:Trucksx:We
x:hasClient
x:sells
13JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
SalesQ2-2012
Sales
x:sells
CAiSE’13Introduction
CAiSE 13
■ Alter the granularity of the ontologiesUse 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:sellsx:hasClient
x:Trucksx:We
x:hasClient
x:sells
14JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
SalesQ2-2012
Sales
x:sells
CAiSE’13Overview
CAiSE 13
■ Introduction Facts with Ontology-valued Measures
□ Base Facts□ Shared Facts
■ OLAP with Ontology-valued MeasuresM□ Merge
□ Abstraction
Implementation■ Implementation■ Summary and Future Work
15JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
AgentsResourcesResources
Events
16JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
AgentsResources
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
17JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:provideAgentsResourcesx:qtySold
100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruckrea:stockflow
x:Moneyrea:stockflow
Resources
x:Germany_Q2 2012_Payment_OurTruckrea:provide
x:revenueEvents
10,200,000
18JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:typerea:provideAgents
Resource x:qtySoldrdf:type
x:ProductModel100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruckrea:stockflow
x:Moneyrea:stockflow
Types x:PaymentType
rdf:type
x:Germany_Q2 2012_Payment_OurTruckrea:provide
x:revenueEvent Groups
rea:Grouprdf:type 10,200,000
19JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:typerea:provideAgents
Resource x:qtySoldrdf:type
x:ProductModel100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruckrea:stockflow
x:Moneyrea:stockflow
Types x:PaymentType
rdf:type
x:Germany_Q2 2012_Payment_OurTruckrea:provide
x:revenueEvent Groups
rea:Grouprdf:type 10,200,000
A li ti M d l
x:Salerea:Event
rdfs:subClassOf
Application Model
rea:Agent
Metamodel Metamodel
x:Sale
x:exchangerdfs:domain
rdfs:range
rea:Event
rdfs:subClassOfrea:provide rea:receive
rdfs:domain
rdfs:range
rdfs:domain
rdfs:range
20JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
x:Payment
rdfs:range
rea:Event
rdfs:range rdfs:range
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:typerea:provideAgents
Resource
x:Sale
rdf:typex:qtySold
rdf:type
x:ProductModel100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruckrea:stockflow
x:Moneyrea:stockflow
Types
rdf:type
x:PaymentType
rdf:type
x:Germany_Q2 2012_Payment_OurTruckrea:provide
x:revenueEvent Groups
rea:Grouprdf:type
rea:Resource
rdf:type
rdf:type
rea:Agent
rdf:typerdf: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:exchangerdfs:domain
rdfs:range
rea:Event
rdfs:subClassOfrea:provide rea:receive
rdfs:domain
rdfs:range
rdfs:domain
rdfs:range
21JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
x:Payment
rdfs:range
rea:Event
rdfs:range rdfs:range
CAiSE’13Base Facts
CAiSE 13
x:Germany_Sales_Q2-2012
rea:Group
rdf:typerea:provideAgents
Resource
x:Sale
rdf:typex:qtySold
rdf:type
x:ProductModel100
x:exchange
x:Germany Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:We
x:Food_Inc
rea:receive
rea:receive
x:OurTruckrea:stockflow
x:Moneyrea:stockflow
Types
rdf:type
x:PaymentType
rdf:type
x:Germany_Q2 2012_Payment_OurTruckrea:provide
x:revenueEvent Groups
rea:Grouprdf:type
rea:Resource
rdf:type
rdf:type
rea:Agent
rdf:typerdf:type
x:Payment
rdf:type
10,200,000
ResourceGroups
Agents
x:Germany_Q2-2012_Sales_FunnySUVs
x:FunnyCarx:SUVsrea:stockflow
rdf:type
rdf:typerdf:typerea:provide
rea:receiverdf:type
rdf:typex:Sale
rdf:type
Resource Types
gx:exchange
x:Germany_Q2-2012_Payment_FunnySUVsx:Families
x:Moneyrea:stockflowAgent Groups
Event Groupsrdf:typerea:provide
rea:receive
rdf:type
rdf:type
rea:Grouprdf:type
22JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
Event Groups
rea:Group
rdf:type
rea:Agent
rdf:type
x:Paymentrea:Group
rdf:typex:PaymentType
CAiSE’13Base Facts
CAiSE 13
23JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Shared 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
24JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
+ competition: RDF
Strategy
CAiSE’13Shared 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:subClassOfrdf: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:groupingrea:grouping
rea:groupingrea:grouping
Time: ‹ all ›
+ competition =x:Sales_Model
: StrategyLocation: ‹ all ›
x:Families
rea:Group
x:Singles
rdf:typerdf:typex:Enterprise
x:Food_Incrdf:type
x:PaymentType
x:Moneyrdf:type
Production: ‹ department ›
Organization: ‹ all ›
x:Production_Model
x:ProductModel
rdf:type
x:ToolModel
rdf:type
25JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
Location: ‹ all › Time: ‹ all ›
+ competition =x:Production_Model
: Strategyx:Enterprise x:Binford
rdf:type
x:OurTruckEngine
x:CleverCarChassis
x:FunnySUVEngine
rdf:type
rdf:typex:BinfordRobot
CAiSE’13Shared 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:subClassOfrdf:type
rdf:type+ metamodel
andcommon
+ 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:OurSUVapplicationmodel
Sales: ‹ department ›
Organization: ‹ all ›
x:Sales_Model
x:Families x:Singles
x:Households
x:OurTruck x:OurSUV
x:SUVsx:Trucks
rea:groupingrea:grouping
rea:groupingrea:grouping
+ salesTime: ‹ all ›
+ competition =x:Sales_Model
: StrategyLocation: ‹ all ›
x:Families
rea:Group
x:Singles
rdf:typerdf:typex:Enterprise
x:Food_Incrdf:type
x:PaymentType
x:Moneyrdf:type
+ sales applicationmodel
Production: ‹ department ›
Organization: ‹ all ›
x:Production_Model
x:ProductModel
rdf:type
x:ToolModel
rdf:type + production
26JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
Location: ‹ all › Time: ‹ all ›
+ competition =x:Production_Model
: Strategyx:Enterprise x:Binford
rdf:type
x:OurTruckEngine
x:CleverCarChassis
x:FunnySUVEngine
rdf:type
rdf:typex:BinfordRobot
+ productionapplicationmodel
CAiSE’13Shared Facts (Inheritance)
CAiSE 13
27JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Overview
CAiSE 13
■ Introduction■ Facts with Ontology-valued Measures
□ Base Facts□ Shared Facts
OLAP with Ontology-valued MeasuresM□ Merge
□ Abstraction
Implementation■ Implementation■ Summary and Future Work
28JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Merge (Union)
CAiSE 13
x:Germany_Sales_Q2-2012
x:Germany_Q2-2012_Sales_OurTruck
x:Werea:provide
x:Food_Inc rea:receive x:OurTruck
rea:stockflowx:France_Sales_Q2-2012
x:France_Q2-2012_Sales_OurSUV
x:Werea:provide
x:Families rea:receive x:OurSUV
rea:stockflow
rea:receive x:OurTruck
x:Germany_Q2-2012_Sales_FunnySUVs
x:FunnyCarrea:provide
x:Families i x:SUVs
rea:stockflow
rea:receive
x:France_Q2-2012_Sales_FunnySUVs
x:FunnyCarrea: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 Increa:receive rea:stockflow
Union
CONSTRUCT { ?s ?p ?o } WHERE {{GRAPH x:Germany Sales Q2 2012 {
x:Germany_Q2-2012_Sales_OurTruck
_
x:We rea:providex:France_Q2-2012_
Sales_OurSUVx:Families rea:receive
rea:provide
x:OurTruck
x:OurSUV
rea:stockflow
rea:stockflow
Union
GRAPH x:Germany_Sales_Q2-2012 {?s ?p ?o
} UNIONGRAPH x:France_Sales_Q2-2012 {?s ?p ?o
}
rea:receivex:Germany_Q2-2012_
Sales_FunnySUVsrea:receive
x:FunnyCar rea:providex:France_Q2-2012_Sales FunnySUVs
kfl
rea:provide
x:SUVs
rea:stockflow
29JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
}}
Sales_FunnySUVsx:Singles
rea:receiverea:stockflow
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SUx: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:Singlesrea:receive
rea:stockflow
rea:provide
30JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SUx: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:Singlesrea:receive
rea:stockflow
rea:provide
31JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SUx: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:Singlesrea:receive
rea:stockflow
rea:provide
32JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
Sa es_Ou SUx: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:Singlesrea:receive
rea:stockflow
rea:provide
RDFS Reasoner
33JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive
x:OurTruckrea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
x:Salerdf:type
rdf:type
Sa es_Ou SUx:Families rea:receive
x:Germany_Q2-2012_Sales_FunnySUVs
rea:receive
x:FunnyCar rea:provide
rea:stockflow
x:SUVsrea:stockflow
x:Salerdf:type
Sales Groups
x:France_Q2-2012_Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Salerdf:type
RDFS Reasoner
34JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive
x:OurTruckrea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
x:Salerdf:type
rdf:type
Sa es_Ou SUx:Families rea:receive
x:Germany_Q2-2012_Sales_FunnySUVs
rea:receive
x:FunnyCar rea:provide
rea:stockflow
x:SUVsrea:stockflow
x:Salerdf:type
Sales Groups
x:France_Q2-2012_Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Salerdf:type
35JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012
F d Irea:receive
x:OurTruckrea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
x:Salerdf:type
rdf:type
Sa es_Ou SUx:Families rea:receive
x:Germany_Q2-2012_Sales_FunnySUVs
rea:receive
x:FunnyCar rea:provide
rea:stockflow
x:SUVsrea:stockflow
x:Salerdf:type
Sales Groups
x:France_Q2-2012_Sales_FunnySUVsx:Singles
rea:receive rea:stockflow
rea:provide x:Salerdf:typex:Households
rea:groupingrea:grouping
x:Families x:Singlesg p g
rdf:typerdf:type
36JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
rea:Group
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012_Abstraction
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
_x:Households
rea:receivex:Germany_Q2-2012_
Sales_FunnySUVsrea: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
CAiSE’13Abstraction
CAiSE 13
x:Europe_Sales_Q2-2012_Abstraction
F d Irea:receive rea:stockflow
x:Germany_Q2-2012_Sales_OurTruck
x:Food_Inc
x:We rea:providex:France_Q2-2012_
Sales OurSUVrea:provide
x:OurTruck
x:OurSUV
rea:stockflow
_x:Households
rea:receivex:Germany_Q2-2012_
Sales_FunnySUVsrea: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
CAiSE’13Merge (Intersection)
CAiSE 13
39JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Merge (Intersection)
CAiSE 13
40JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Overview
CAiSE 13
■ Introduction■ Facts with Ontology-valued Measures
□ Base Facts□ Shared Facts
■ OLAP with Ontology-valued MeasuresM□ Merge
□ Abstraction
Implementation Implementation■ Summary and Future Work
41JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Implementation
CAiSE 13
■ Based on hetero-homogeneous data warehousehtt //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
CAiSE’13Overview
CAiSE 13
■ Introduction■ Facts with Ontology-valued Measures
□ Base Facts□ Shared Facts
■ OLAP with Ontology-valued MeasuresM□ Merge
□ Abstraction
Implementation■ Implementation Summary and Future Work
43JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering
CAiSE’13Summary 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 measuresIn particular: e3value and its variants, e.g., e3forces
■ Provide for easier querying, examine other query languages
44JKU Linz Institut für Wirtschaftsinformatik – Data & Knowledge Engineering