Advantages of UML for Multidimensional Modeling
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1 Advantages of UML for Multidimensional Modeling Sergio Luján-Mora Juan Trujillo Department of Software and Computing Systems University of Alicante (Spain) Panos Vassiliadis Department of Computer Science University of Ioannina (Greece) Content Introduction MD Modeling based on the UML Conclusions 2 1
2 Content Introduction MD Modeling based on the UML Conclusions 3 Introduction Data warehouses (DW) Multidimensional databases (MDB) On-Line Analytical Processing (OLAP) Multidimensional paradigm Facts Measures Dimensions Attributes Different approaches No standard 4 2
3 Introduction Unified Modeling Language (UML) Standard object-oriented (OO) modeling language for describing and designing software systems Widely accepted General Visual support 5 Our previous work: Introduction A UML extension for MD modeling Based on a three-layered schema In this work: How our approach resolves important problems of MD modeling: multistar models, shared dimensions, multiple and alternative hierarchy levels, etc. 6 3
4 Content Introduction MD Modeling based on the UML Conclusions 7 MD Modeling based on the UML UML is a general purpose visual modeling language for systems Extension mechanisms allow the user to tailor it to specific domains Mechanisms: Stereotypes New building elements Tagged values New properties Constraints New semantics 8 4
5 MD Modeling based on the UML Stereotype is the main mechanism: Tagged values define stereotype properties Constraints specify stereotype behavior Different representations of a stereotype: Icon Decoration Label None 9 Model Star schema Dimension/fact definition definition definition 10 5
6 MD Modeling based on the UML Package stereotypes Class stereotypes StarPackage (Level 1) Fact (Level 3) FactPackage (Level 2) Dimension (Level 3) DimensionPackage (Level 2) Base (Level 3) 11 Content Introduction MD Modeling based on the UML Advantages of UML for MD Modeling Conclusions 12 6
7 1. Multistar models 2. Support for different building perspectives 3. Shared dimensions 4. Shared hierarchy levels 5. Multiple and alternative classification hierarchies Heterogeneous dimensions 7. Shared aggregation 8. Derivation rules 9. Computer-Aided Software Engineering (CASE) tool support 14 7
8 Running example: a simplified version of the warehouse example from Kimball s The Data Warehouse Toolkit: Inventory snapshot: measures the inventory levels in a regular period of time Delivery status inventory: tracks the disposition of all the items in a delivery Transaction inventory: every change of status of delivered products is recorded throughout the delivery flow Multistar models: Fact constellation A single MD model has multiple facts Multiple star schemas 16 8
9 2. Support for different building perspectives: Top-down: DW data marts Bottom-up: data marts DW Shared dimensions: Two or more star schemas can share some dimensions Benefits: Reduces the development time Drill-across Avoids inconsistency 18 9
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12 4. Shared hierarchy levels: Two or more dimensions share some hierarchy levels Similar benefits to shared dimensions: reduces development time, avoids inconsistency, etc. Salient feature: two dimensions do not need to share the whole hierarchy Higher level of flexibility
13 5. Multiple and alternative classification hierarchies: Classification hierarchies provide the basis for the subsequent data analysis Multiple: when a dimension has two or more classification hierarchies Alternative: where two or more classification hierarchies converge into the same hierarchy level
14 6. Heterogeneous dimensions: A dimension that describes a large number of heterogeneous items with different attributes Different categorization levels thanks to generalization/specialization hierarchies
15 7. Shared aggregation: Allows us to represent many-to-many relationships between Fact and Dimension Named relationships allows us to define more than one relationship between two classes
16 8. Derivation rules: Derived attribute / Derivation rules are explicitly defined
17 9. Computer-Aided Software Engineering (CASE) tool support: Add-in for Rational Rose 2002 A well-known CASE tool available in the market We do not have to develop our own CASE tool from scratch
18 Content Introduction MD Modeling based on the UML Conclusions 35 Conclusions UML packages: clean MD design of huge and complex systems UML importation mechanism: Simplifies the models Avoids redundancy, inconsistency, and ambiguity 36 18
19 Future work: Conclusions Implementation of MD models ORDB and OODB Different implementation strategies: snowflake, constellation, etc. Incorporate UML use cases into MD conceptual modeling 37 Advantages of UML for Multidimensional Modeling Sergio Luján-Mora Juan Trujillo Department of Software and Computing Systems University of Alicante (Spain) Panos Vassiliadis Department of Computer Science University of Ioannina (Greece) 38 19
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