lucentia research group department of software and computing systems using i* modeling for the...
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LUCENTIA Research GroupDepartment of Softwareand Computing Systems
Using i* modeling for the Using i* modeling for the multidimensional design of multidimensional design of data warehousesdata warehousesJose-Norberto Mazón, [email protected]
Juan Trujillo, [email protected]
Toronto, 17th July 2008
Using i* modeling for the multidimenionaldesign of data warehouses2
ContentsContents
• Introduction• Current research
• Requirements for DWs• Reconciling with data sources• Deriving logical representations
• Conclusions and short term research
Using i* modeling for the multidimenionaldesign of data warehouses3
ContentsContents
• Introduction• Current research
• Requirements for DWs• Reconciling with data sources• Deriving logical representations
• Conclusions and short term research
Using i* modeling for the multidimenionaldesign of data warehouses4
IntroductionIntroductionResearch problemResearch problem
• Data warehouse• Integrated collection of historical data in support of decision
making process
• Multidimensional (MD) modeling• Fact
• Contains interesting measures of a business process
• Dimension• Represents context of analysis
• Resembles traditional method for database design• Model at conceptual level
• Abstracting details related to specific technologies
Using i* modeling for the multidimenionaldesign of data warehouses5
DATASOURCES
INTERNAL
EXTERNAL
DATAWAREHOUSE
ETL CUBES
OLAP
DATAMINING
REPORTS
WHAT-IFANALYSIS
- Integrated collection of historical datain support of decision makers
IntroductionIntroductionResearch problemResearch problem
Using i* modeling for the multidimenionaldesign of data warehouses6
DATASOURCES
INTERNAL
EXTERNAS
DATAWAREHOUSE
ETL CUBES
OLAP
DATAMINING
REPORTS
WHAT-IFANALYSIS
DATASOURCES
- Integrated collection of historical datain support of decision makers
IntroductionIntroductionResearch problemResearch problem
Using i* modeling for the multidimenionaldesign of data warehouses7
DATASOURCES
INTERNAL
EXTERNAS
DATAWAREHOUSE
ETL CUBES
OLAP
DATAMINING
REPORTS
WHAT-IFANALYSIS
DATASOURCES
- Integrated collection of historical datain support of decision makers
- Information needs cannot be understoodby only analyzing data sources
IntroductionIntroductionResearch problemResearch problem
Using i* modeling for the multidimenionaldesign of data warehouses8
DATASOURCES
INTERNAL
EXTERNAS
DATAWAREHOUSE
ETL CUBES
OLAP
DATAMINING
REPORTS
DECISIONMAKERS
DATASOURCES
- Integrated collection of historical datain support of decision makers
IntroductionIntroductionResearch problemResearch problem
- Information needs cannot be understoodby only analyzing data sources
Using i* modeling for the multidimenionaldesign of data warehouses9
WHAT-IFANALYSIS
DATASOURCES
INTERNAL
EXTERNAS
DATAWAREHOUSE
ETL CUBES
OLAP
DATAMINING
REPORTS
WHAT-IFANALYSIS
DECISIONMAKERS
DATASOURCES
- Integrated collection of historical datain support of decision makers
IntroductionIntroductionResearch problemResearch problem
- Information needs cannot be understoodby only analyzing data sources
- Decision makingprocesses mustbe understood bydesigners
Using i* modeling for the multidimenionaldesign of data warehouses10
• Only data sources are analyzed to define the conceptual MD model• Incorrect information needs may be modeled
• Requirements are specified once the conceptual MD model is defined (even after the deployment of the DW)• Incorrect MD elements may be modeled
• Requirements and data sources are not reconciled• Complex ETL processes to populate the DW
• Thus, the DW is not viewed as a valuable resource
IntroductionIntroductionDrawbacks of the state-of-the-artDrawbacks of the state-of-the-art
Using i* modeling for the multidimenionaldesign of data warehouses11
• 1. Explicit requirement analysis stage• Focus on decision making processes• Information requirements
• 2. Transformation to a conceptual MD model• Model Driven approach• MD model agrees with decision makers’ expectations
• 3. Reconcile requirement model with data sources• MD model agrees with data sources
• Completeness
• Faithfulness
IntroductionIntroductionNovelty of our proposalNovelty of our proposal
Using i* modeling for the multidimenionaldesign of data warehouses12
• 1. Explicit requirement analysis stage• Focus on decision making processes• Information requirements
• 2. Transformation to a conceptual MD model• Model Driven approach• MD model agrees with decision makers’ expectations
• 3. Reconcile requirement model with data sources• MD model agrees with data sources
• Completeness• Faithfulness
IntroductionIntroductionNovelty of our proposalNovelty of our proposal
Using i* modeling for the multidimenionaldesign of data warehouses13
• 1. Explicit requirement analysis stage• Focus on decision making processes• Information requirements
• 2. Transformation to a conceptual MD model• Model Driven approach• MD model agrees with decision makers’ expectations
• 3. Reconcile requirement model with data sources• MD model satisfies decision makers’ needs• MD model agrees with data sources
• Completeness• Faithfulness
IntroductionIntroductionNovelty of our proposalNovelty of our proposal
Using i* modeling for the multidimenionaldesign of data warehouses14
• Defining a goal-oriented approach for DWs• Based on i*• Model decision processes
• Decision makers are concerned about GOALS not directly DATA
• Traceability to a conceptual MD model• Align with MDA
• Integrate requirements and data sources
IntroductionIntroductionObjectives of our proposalObjectives of our proposal
Using i* modeling for the multidimenionaldesign of data warehouses15
MDAMDA
• Model Driven Architecture (MDA)• Object Management group (OMG) standard• Using models in software development
• Computation Independent Model (CIM)• Platform Independent Model (PIM)• Platform Specific Model (PSM)
• Transformations between models• Query/View/Transformation language (QVT)
• The code is obtained from PSMs
Using i* modeling for the multidimenionaldesign of data warehouses16
PIM
PSM1 PSMN
CIM
T T
CODE1
T
CODEN
T
T
...
...
MDAMDA
• Model Driven Architecture (MDA)
Describes user requirements
Contains information about functionality andstructure of the system without taking into account
the technology used to implement it
Includes information about the specifictechnology that is used in the implementation
of the system on a specific platform
Every PSM is transformed into code to beexecuted, obtaining the final software product.
Using i* modeling for the multidimenionaldesign of data warehouses17
MDAMDA
• Query/View/Transformation language (QVT) • Declarative part of QVT• Transformation set of relations• Relations between metamodels formally defined and
automatically performed• Relations applied to models
Using i* modeling for the multidimenionaldesign of data warehouses18
MDAMDA
MODEL 1
MODEL2
R DOMAINCANDIDATE
MODEL
WHEN & WHERECLAUSES
KIND OF RELATION
METAMODELNAME
Declarative approach of QVT specifies relationships that
must hold between candidate models
Using i* modeling for the multidimenionaldesign of data warehouses19
IntroductionIntroductionOur proposalOur proposal
[REBNITA 2005][RIGIM 2007]
[ER 2006][ER 2007]
[DKE 2007]
[DOLAP 2005][DaWaK 2006]
[DSS 2008]
Using i* modeling for the multidimenionaldesign of data warehouses20
ContentsContents
• Introduction• Current research
• Requirements for DWs• Reconciling with data sources• Deriving logical representations
• Conclusions and short term research
Using i* modeling for the multidimenionaldesign of data warehouses21
• Goal Oriented Requirement Engineering• DW supports the decision making process to fulfill
goals of an organization• Decision makers are concerned about goals
• Information requirements are obtained by refining decision makers’ goals
• MDA approach• Information requirements must be derived into a
conceptual MD model
Requirements for DWsRequirements for DWs
Using i* modeling for the multidimenionaldesign of data warehouses22
• CIM• Goals and information
requirements
• PIM• Conceptual MD model
• QVT• Transformation between
models
Requirements for DWsRequirements for DWs
Using i* modeling for the multidimenionaldesign of data warehouses23
Requirements for DWsRequirements for DWsDefining a CIMDefining a CIM
• Classification of DW goals• Strategic goals
• Change to a better situation
• Decision goals• Take appropiate actions
• Information goals• Related to required information
• Information requirements• Interesting measures of business process• Context of analysis
Using i* modeling for the multidimenionaldesign of data warehouses24
• i* framework• Modeling goals of decision makers and the required
tasks and resources to fulfil them• Several decision makers with different goals
• Two extensions of UML• Profile for i*• Profile for adapting i* to the DW domain
Requirements for DWsRequirements for DWsDefining a CIMDefining a CIM
Using i* modeling for the multidimenionaldesign of data warehouses25
Requirements for DWsRequirements for DWsDefining a CIMDefining a CIM
Using i* modeling for the multidimenionaldesign of data warehouses26
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses27
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses28
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses29
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses30
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses31
Requirements for DWsRequirements for DWsSample CIMSample CIM
Using i* modeling for the multidimenionaldesign of data warehouses32
Conceptual MD modelConceptual MD model
• UML Profile for MD modeling• Luján, Trujillo, Song. A
UML profile for Multidimensional Modeling in Data Warehouses. Data and Knowledge Engineering. 2006.
• Class diagram
Stereotype Icon
Fact
Dimension
Base
FactAttribute
DimensionAttribute
Rolls-UpTo <<Rolls-UpTo>>
Using i* modeling for the multidimenionaldesign of data warehouses33
Conceptual MD modelConceptual MD model
Using i* modeling for the multidimenionaldesign of data warehouses34
Conceptual MD modelConceptual MD modelObtaining an initial PIMObtaining an initial PIM
Using i* modeling for the multidimenionaldesign of data warehouses35
Conceptual MD modelConceptual MD modelObtaining an initial PIMObtaining an initial PIM
Using i* modeling for the multidimenionaldesign of data warehouses36
Conceptual MD modelConceptual MD modelSample initial PIMSample initial PIM
Using i* modeling for the multidimenionaldesign of data warehouses37
USER REQUIREMENTS
DATASOURCES
RECONCILIATION
PIM
PSM
INITIALPIM
Reconciling with data sourcesReconciling with data sources
Using i* modeling for the multidimenionaldesign of data warehouses38
Reconciling with data sourcesReconciling with data sources
• The MD conceptual model is reconciled with the available data sources• The DW will be properly populated from data sources• The analysis potential provided by the data sources is captured by the
MD conceptual model• Redundancies are avoided• Optional dimension levels are controlled to enable summarizability and
to avoid inconsistent queries
• Reconciliating process is automatically performed• QVT relations based on Multidimensional Normal Forms
• Lechtenbörger and Vossen. Multidimensional normal forms for data warehouse design. Information Systems 28(2003)
Using i* modeling for the multidimenionaldesign of data warehouses39
Reconciling with data sourcesReconciling with data sources
Using i* modeling for the multidimenionaldesign of data warehouses40
n_t1=district, n_t2=state
<<Rolls-upTo>>
1..n +d
1
+r
Reconciling with data sourcesReconciling with data sources
Using i* modeling for the multidimenionaldesign of data warehouses41
Deriving logical representationsDeriving logical representations
• PIM• UML profile for MD modeling [Luján et al. DKE 2006]
• PSM• Common Warehouse Metamodel (CWM)
• From PIM to each PSM • QVT transformation
Using i* modeling for the multidimenionaldesign of data warehouses42
• Common Warehouse Metamodel (CWM)• Resource layer
• Standard to represent the structure of data according to certain technologies
• Relational metamodel• Tables, columns, primary keys, and so on
• Multidimensional metamodel• Generic data structures • Vendor specific extension
• Oracle Express extension
Deriving logical representationsDeriving logical representations
Using i* modeling for the multidimenionaldesign of data warehouses43
ContentsContents
• Introduction• Current research
• Requirements for DWs• Reconciling with data sources• Deriving logical representations
• Conclusions and short term research
Using i* modeling for the multidimenionaldesign of data warehouses44
• DW projects fail in support decision making process• Requirement analysis stage is overlooked for defining
a conceptual MD model
• Using i* framework together with MDA
ConclusionsConclusionsObjectivesObjectives
Using i* modeling for the multidimenionaldesign of data warehouses45
• MDA framework• UML profile for i*
• Extension for using i* in the DW domain
• Transformations to obtain a conceptual MD model• Several kind of logical representations
• Multidimensional normal forms• Reconciling data sources and requirements in a hybrid approach
• Eclipse-based prototype
ConclusionsConclusionsScientific contributionsScientific contributions
Using i* modeling for the multidimenionaldesign of data warehouses46
Eclipse-based prototypeEclipse-based prototype
Using i* modeling for the multidimenionaldesign of data warehouses47
ConclusionsConclusionsRelated work at LUCENTIA research groupRelated work at LUCENTIA research group
UML Profile forMD Modeling at DKE 2006
UML for PhysicalModeling at JCIS 2006
Common WarehouseMetamodel
CIM
PIM
PSM
MDA [DKE 2007 & DSS 2008]
Requirements for DWs[RIGiM 2007]
Security[DSS 2006 & IS 2007]
UML profile for Data mining[DKE 2007]
Data sources analysis[ER 2007]
Using i* modeling for the multidimenionaldesign of data warehouses48
• Studying unstructured decision processes in-deth to model them in i* diagrams• Taking advantage of every i* feature• Considering complex mechanisms to reason
about goals and structure decision processes• Prioritization of goals
Short term researchShort term research
LUCENTIA Research GroupDepartment of Softwareand Computing Systems
Using i* modeling for the Using i* modeling for the multidimensional design of multidimensional design of data warehousesdata warehousesJose-Norberto Mazón, [email protected]
Juan Trujillo, [email protected]
Toronto, 17th July 2008