1. LUCENTIA Research Group Department of Software and Computing Systems Using i* modeling for the multidimensional design of data warehouses Jose-Norberto Mazón, jnmazon@dlsi.ua.es Juan Trujillo, jtrujillo@dlsi.ua.es Toronto, 17 th July 2008

2. Using i* modeling for the multidimenional design of data warehouses 2 Contents Introduction Current research Requirements for DWs Reconciling with data sources Deriving logical representations Conclusions and short term research

3. Using i* modeling for the multidimenional design of data warehouses 3 Contents Introduction Current research Requirements for DWs Reconciling with data sources Deriving logical representations Conclusions and short term research

4. Using i* modeling for the multidimenional design of data warehouses 4 Introduction Research 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

5. Using i* modeling for the multidimenional design of data warehouses 5 DATA SOURCES INTERNAL EXTERNAL DATA WAREHOUSE ETL CUBES OLAP DATA MINING REPORTS WHAT-IF ANALYSIS - Integrated collection of historical data in support of decision makers Introduction Research problem

6. Using i* modeling for the multidimenional design of data warehouses 6 DATA SOURCES INTERNAL EXTERNAS DATA WAREHOUSE ETL CUBES OLAP DATA MINING REPORTS WHAT-IF ANALYSIS DATA SOURCES - Integrated collection of historical data in support of decision makers Introduction Research problem

7. Using i* modeling for the multidimenional design of data warehouses 7 DATA SOURCES INTERNAL EXTERNAS DATA WAREHOUSE ETL CUBES OLAP DATA MINING REPORTS WHAT-IF ANALYSIS DATA SOURCES - Integrated collection of historical data in support of decision makers - Information needs cannot be understood by only analyzing data sources Introduction Research problem

8. Using i* modeling for the multidimenional design of data warehouses 8 DATA SOURCES INTERNAL EXTERNAS DATA WAREHOUSE ETL CUBES OLAP DATA MINING REPORTS DECISION MAKERS DATA SOURCES - Integrated collection of historical data in support of decision makers Introduction Research problem - Information needs cannot be understood by only analyzing data sources

9. Using i* modeling for the multidimenional design of data warehouses 9 WHAT-IF ANALYSIS DATA SOURCES INTERNAL EXTERNAS DATA WAREHOUSE ETL CUBES OLAP DATA MINING REPORTS WHAT-IF ANALYSIS DECISION MAKERS DATA SOURCES - Integrated collection of historical data in support of decision makers Introduction Research problem - Information needs cannot be understood by only analyzing data sources - Decision making processes must be understood by designers

10. Using i* modeling for the multidimenional design of data warehouses 10 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 Introduction Drawbacks of the state-of-the-art

11. Using i* modeling for the multidimenional design of data warehouses 11 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 Introduction Novelty of our proposal

12. Using i* modeling for the multidimenional design of data warehouses 12 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 Introduction Novelty of our proposal

13. Using i* modeling for the multidimenional design of data warehouses 13 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 Introduction Novelty of our proposal

14. Using i* modeling for the multidimenional design of data warehouses 14 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 Introduction Objectives of our proposal

15. Using i* modeling for the multidimenional design of data warehouses 15 MDA 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

16. Using i* modeling for the multidimenional design of data warehouses 16 MDA Model Driven Architecture (MDA) Describes user requirements Contains information about functionality and structure of the system without taking into account the technology used to implement it Includes information about the specific technology that is used in the implementation of the system on a specific platform Every PSM is transformed into code to be executed, obtaining the final software product.

17. Using i* modeling for the multidimenional design of data warehouses 17 MDA 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

18. Using i* modeling for the multidimenional design of data warehouses 18 MDA MODEL 1 MODEL2 R DOMAIN CANDIDATE MODEL WHEN & WHERE CLAUSES KIND OF RELATION METAMODEL NAME Declarative approach of QVT specifies relationships that must hold between candidate models

19. Using i* modeling for the multidimenional design of data warehouses 19 Introduction Our proposal [REBNITA 2005] [RIGIM 2007] [ER 2006] [ER 2007] [DKE 2007] [DOLAP 2005] [DaWaK 2006] [DSS 2008]

20. Using i* modeling for the multidimenional design of data warehouses 20 Contents Introduction Current research Requirements for DWs Reconciling with data sources Deriving logical representations Conclusions and short term research

21. Using i* modeling for the multidimenional design of data warehouses 21 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 DWs

22. Using i* modeling for the multidimenional design of data warehouses 22 CIM Goals and information requirements PIM Conceptual MD model QVT Transformation between models Requirements for DWs

23. Using i* modeling for the multidimenional design of data warehouses 23 Requirements for DWs Defining 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

24. Using i* modeling for the multidimenional design of data warehouses 24 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 DWs Defining a CIM

25. Using i* modeling for the multidimenional design of data warehouses 25 Requirements for DWs Defining a CIM

26. Using i* modeling for the multidimenional design of data warehouses 26 Requirements for DWs Sample CIM

27. Using i* modeling for the multidimenional design of data warehouses 27 Requirements for DWs Sample CIM

28. Using i* modeling for the multidimenional design of data warehouses 28 Requirements for DWs Sample CIM

29. Using i* modeling for the multidimenional design of data warehouses 29 Requirements for DWs Sample CIM

30. Using i* modeling for the multidimenional design of data warehouses 30 Requirements for DWs Sample CIM

31. Using i* modeling for the multidimenional design of data warehouses 31 Requirements for DWs Sample CIM

32. Using i* modeling for the multidimenional design of data warehouses 32 Conceptual 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 StereotypeIcon Fact Dimension Base FactAttribute DimensionAttribute Rolls-UpTo >

33. Using i* modeling for the multidimenional design of data warehouses 33 Conceptual MD model

34. Using i* modeling for the multidimenional design of data warehouses 34 Conceptual MD model Obtaining an initial PIM

35. Using i* modeling for the multidimenional design of data warehouses 35 Conceptual MD model Obtaining an initial PIM

36. Using i* modeling for the multidimenional design of data warehouses 36 Conceptual MD model Sample initial PIM

37. Using i* modeling for the multidimenional design of data warehouses 37 USER REQUIREMENTS DATA SOURCES RECONCILIATION PIM PSM INITIAL PIM Reconciling with data sources

38. Using i* modeling for the multidimenional design of data warehouses 38 Reconciling 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)

39. Using i* modeling for the multidimenional design of data warehouses 39 Reconciling with data sources

40. Using i* modeling for the multidimenional design of data warehouses 40 n_t1=district, n_t2=state > 1..n +d 1 +r Reconciling with data sources

41. Using i* modeling for the multidimenional design of data warehouses 41 Deriving 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

42. Using i* modeling for the multidimenional design of data warehouses 42 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 representations

43. Using i* modeling for the multidimenional design of data warehouses 43 Contents Introduction Current research Requirements for DWs Reconciling with data sources Deriving logical representations Conclusions and short term research

44. Using i* modeling for the multidimenional design of data warehouses 44 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 Conclusions Objectives

45. Using i* modeling for the multidimenional design of data warehouses 45 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 Conclusions Scientific contributions

46. Using i* modeling for the multidimenional design of data warehouses 46 Eclipse-based prototype

47. Using i* modeling for the multidimenional design of data warehouses 47 Conclusions Related work at LUCENTIA research group UML Profile for MD Modeling at DKE 2006 UML for Physical Modeling at JCIS 2006 Common Warehouse Metamodel 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]

48. Using i* modeling for the multidimenional design of data warehouses 48 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 research

49. LUCENTIA Research Group Department of Software and Computing Systems Using i* modeling for the multidimensional design of data warehouses Jose-Norberto Mazón, jnmazon@dlsi.ua.es Juan Trujillo, jtrujillo@dlsi.ua.es Toronto, 17 th July 2008