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Transformation Patterns

Transformation Patterns. Al, Jean, Jeff, Kerry, Krzysztof, William, ??. Transformation Patterns. What is a transformation pattern? common patterns I have seen structures. Examples. MOF 1.3  EMF more constructs in MOF than in EMF want bi-directional (EMF  MOF 1.3)

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Transformation Patterns

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  1. Transformation Patterns Al, Jean, Jeff, Kerry, Krzysztof, William, ??

  2. Transformation Patterns • What is a transformation pattern? • common patterns I have seen • structures

  3. Examples • MOF 1.3  EMF • more constructs in MOF than in EMF • want bi-directional (EMF  MOF 1.3) • e.g. MOF constraint into EMF annotation • annotation had information on how to go back to EMF • Details • a) Normal EMF  MOF is pretty easy • b) But if the annotations have been changed, completing the round-trip is a serious problem • Only allow round-trip if it is consistent; otherwise (a)

  4. More Examples • UML to MOF • UML XMI transform to MOF XMI • example of a tool writing in ATL • UML Activity to BPEL • MOF to JMI • CGI to EJB • JavaDoc • Genealogy data

  5. More Examples… • Cross-compilation • OQL to SQL • etc • Databases • Normalizations • Logical to physical

  6. Common pattern B A C Y X Z

  7. Concrete Example:Database Logical  Physical Class Table Attribute Column

  8. Characterizing Transformationsas a whole • 7) HOT (higher order transformation) • 6) Ma  Mb (nothing) • 5) Ma  Mb Exists MMa – MMp – MMb • 4) Ma  Mb MMa “contains” MMb • 3) Ma  Mb MMa “in” MMb • 2) Ma  Mb MMa = MMb • 1) Ma  Ma (within same model)

  9. optimizations codeexpansion Vertical vs. Horizontal Transformations

  10. Kinds of Transformation • Property-Preserving • property P we have P(Ma) implies P(Mb) when Ma  Mb • Evolution • platform Ma:MMa  Ma’:MMa’ semantics-preserving: Meaning(Ma) = Meaning (Mb) • functional (change in requirements) Ma:MMa  Mb:MMa • Compilation • generally semantics-preserving • Optimization • one that is P-preserving, Q-reducing • Refactoring • changing structure, semantics preserving • Its like also optimization (Q is “-ility” or “niceness” metric)

  11. Source  Target • Cardinality • all cases • 1:1 1:M M:1 M:M • Effect at type level • Does it increase or decrease number of types • For case ?:M • Homogeneous • Heterogeneous

  12. Explosion/Contraction • State Explosion • Two states • adding intermediate states into a transition • Permutation • Elaborating all combinations of information • Version explosion • multiple models coming in that are variants of each other • Like Query languages • projection • selection • order • transitive closure

  13. Philosophical Interlude • What is different from Functional programming? • In functional programming • main structure is a tree • In Model-driven approach • main structure is a graph • moving from trees to graphs gives more power • (but text can be better than pictures for some uses) • All the meta-models are OO • Working with OO structures – graphs

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