M. Wimmer, G. Kappel, Angelika Kusel , W. Retschitzegger , J. Schönböck, W. Schwinger

1. Towards an Expressivity Benchmark for Mappings based on a Systematic Classification of Heterogeneities M. Wimmer, G. Kappel, Angelika Kusel, W. Retschitzegger, J. Schönböck, W. Schwinger Johannes Kepler University Linz, Austria kusel@bioinf.jku.at Thisworkhasbeenpartlyfundedbythe Austrian Science Fund (FWF) undergrant P21374-N13.

2. Motivation Example Heterogeneities Homepage Future Work Motivation (1/2) • Multitudeofmodelingtoolsavailable • Seamlessexchangeofmodels essential • Thus, M2M Transformationstoovercomeheterogeneitiesareneeded Model Creation Tools Model Checking Tools Model Simulation Tools Model Transformation Tools Code Generation Tools

3. Motivation Example Heterogeneities Homepage Future Work Motivation (2/2) • Possibilitiesforexpressingthetransformation: • Use a model transformationlanguage, e.g., ATL • Use a mappingtoolandgeneratethetransformationcode • Advantages • Abstractsfromcode • Reusestransformationlogicthroughreusablecomponents P3: Publication P1: Publication P2: Publication Publication unique Publication kind 1..1 title = ‘P2‘ title = ‘P3‘ title = ‘P1‘ title:String name:String Still unclear • Which kinds of heterogeneities mightoccur between metamodels? • And thus • Which reusable components are needed? • Goal of this work • Analyze potential heterogeneities • Build a systematic classification • Provide benchmark examples wrt. expressivity for mapping tools kind:String MM of Tool2 MM of Tool1 • Howtoexpress such a transformation? instanceof K1: Kind K2: Kind Kind instanceof name= ‘Journal‘ name:String name= ‘Conference‘ P1:Publication name = ‘P1‘ kind = ‘Journal‘ P2:Publication kind name = ‘P2‘ kind kind kind = ‘Journal‘ P3:Publication name = ‘P3‘ kind = ‘Conference‘ Exemplary Model Exemplary Model

4. Motivation Example Heterogeneities Homepage Future Work HeterogeneityExample : EAttribute : EAttribute : EAttribute : EClass : EClass name = ‘name‘ name = ‘title‘ name = ‘name‘ MM of Tool1 MM of Tool2 name = ‘Publication‘ name = ‘Publication‘ lowerBound = 1 lowerBound = 1 lowerBound = 1 Publication upperBound = 1 upperBound = 1 upperBound = 1 abstract = false abstract = false Publication unique title:String Concrete Syntax kind 1..1 name:String kind:String : EClass Noheterogeneity name = ‘Kind‘ • Whichkindsofheterogeneitiesdo occur in thisexample? • WhichkindsofheterogeneitiesmightoccurbetweenEcore-based MMs? Kind abstract = false eStructuralFeatures name:String eStructuralFeatures NamingDifference eStructuralFeatures : EReference Modeling ConceptDifference InstancesofEcore (Abstract Syntax) eReferenceType NamingDifferenceandContextDifference Modeling ConceptDifference name = ‘kind‘ ordered = false lowerBound = 1 : EAttribute upperBound = 1 name = ‘kind‘ eStructuralFeatures containment = false lowerBound = 1 eStructuralFeatures upperBound = 1

5. Motivation Example Heterogeneities Homepage Future Work Publication Variation Points in Ecore-based MMs Publication Paper Publication name:String kind:Integer title:String kind:String Example – Order Difference Example – MultiplicityDifference Example – DirectionDifference Example – Containment Difference Example – ConcretenessDifference Example – InheritanceDifferences Example – DatatypeDifference Example – NamingDifference Example – ContextDifference EClassifier ENamedElement EClass ETypedElement EReference EAttribute EStructuralFeature EDataType NamingDifference Publication Publication Publication Publication abstract : boolean name : String … Publication Publication Publication Publication Publication Publication Publication Publication Publication name:String name:String name:String name:String name:String name:String name:String name:String name:String name:String name:String name:String name:String Inheritance Type Difference auths auths auths pubs pubs auths pubs auths kind MM of Tool1 MM of Tool1 MM of Tool1 MM of Tool1 MM of Tool1 MM of Tool1 MM of Tool1 MM of Tool2 MM of Tool2 MM of Tool2 MM of Tool2 MM of Tool2 MM of Tool2 MM of Tool2 1..* 1..* 1..* 1..* 1..1 1..5 1..* 1..* 1..* Order Difference Conference Conference TechReport Conference Conference TechReport TechReport Two-Column BreadthDifference ordered : boolean location:String location:String location:String location:String university:String university:String university:String lowerBound : int MultiplicityDifference MM of Tool1 MM of Tool2 DepthDifference upperBound : int Author Kind Author Author Author Author Author Author Author • In which combinations might the heterogeneities occur? name:String name:String name:String name:String name:String name:String kind:String name:String name:String ConcretenessDifference ACM Springer ordered eSuperTypes 0..* … MM of Tool1 MM of Tool2 Publication 1..1 ContextDifference 1..1 eAttributeType name:String eReferenceType DirectionDifference kind:String eStructuralFeatures 0..* DatatypeDifference containment : boolean … Containment Difference …

6. Motivation Example Heterogeneities Homepage Future Work Potential Combinations of Heterogeneities Different casescanbedistinguished • SameEcoreconcepts • DifferentEcoreconcepts • DifferentnumberofEcoreconcepts • Additionally: valid instanceset • Differentnumberof valid instances • Difference in theinterpretationoftheinstancevalues Journal Publication Author name:String title:String name:String unique Publication Publication kind 1..1 name:String name:String kind:String kind:String SyntacticHeterogeneities Kind University name:String name:String Author uni 1..1 name:String university:String SemanticHeterogeneities Example – Kind ofConceptDifference Example – Numberof InstancesDifference Example – NumberofConceptsDifference MM of Tool1 MM of Tool2 MM of Tool1 MM of Tool1 MM of Tool2 MM of Tool2

7. Source-Target-Concept Cardinality Disjoint Intersection Subset Superset Same Meta- modelingConcept Different Meta- modelingConcept Different Meta- modelingConcept Same Meta- modelingConcept InheritanceDifference Core ConceptDifference C2C I2I C2A I2C 2R R2I A2I 1:1 m:n 1:n A2A R2R A2C I2A R2C I2R C2I R2A n:1 Containment Difference Context Difference Context Difference Datatype Difference Order Difference Direction Difference Order Difference Multiplicity Difference Multiplicity Difference Concreteness Difference Inheritance Type Difference Depth Difference Breadth Difference Motivation Example Heterogeneities Homepage Future Work Feature-basedClassificationofHeterogeneities Heterogeneity Required Feature • Classification bases on existing work (mainly from the area of data engineering) • F. Legler and F. Naumann. A Classication of Schema Mappings and Analysis of Mapping Tools. In Proc. of BTW'07, 2007. • V. KashyapandA. Sheth. Semanticandschematicsimilaritiesbetweendatabaseobjects: A context-basedapproach. VLDB Journal, 5(4):276-304, 1996. • Contribution • Adaptation totheareaofMDE • Conceptofrelationship • Conceptofinheritance • Systematization by means of a feature model Optional Feature SemanticHeterogeneity SyntacticHeterogeneity XOR Features OR Features Legend StructuralDifference Numberof InstancesDifference NamingDifference Interpretation of Instance Values Difference

8. Motivation Example Heterogeneities Homepage Future Work Benchmark Examples • Shouldserveasexpressivitybenchmarkformappingtools • Communityisinvitedtocontribute!http://www.modeltransformation.net/ Initial setofbenchmarkexamples Form-basedentryofnewexamples Ratingsofexamples Examplesareclassified

9. Motivation Example Heterogeneities Homepage Future Work Future Work • Extendthebenchmarkexamplestofully cover theclassification • Applythebenchmarkexamplesto diverse mappingtoolsfromtheareaof • Model engineering • Data engineering • Ontologyengineering • Extendownmappinglanguagetoprovidetherequiredexpressivity

10. Thank you for your attention! Questions?