1. Merging Models Based on Given Correspondences Rachel A. Pottinger Philip A. Bernstein

2. Introduction “A model is a formal description of a complex application artifact, such as a database schema, an application interface, a UML model, an ontology, or a message format. The problem of merging such models lies at the core of many meta data applications.”

3. Introduction Combining models requires two steps: Determining correspondences between two models (Schema matching) Merging the models based on those correspondences Determining correspondences is a major topic of ongoing research and is not covered in this paper

4. Model Management Proposed by Bernstein in “Applying Model Management to Classical Meta Data Problems” Operators: Match Merge Apply Diff

5. Model Management Presented solution: Merge (A, B, Map AB ) A & B models Map AB = mapping of correspondences Returns “duplicate-free union” of A & B

6. Example - Conflict

7. Conflict Resolution Conflict resolution is independent of representation Existing similarities among solutions offer an opportunity for abstraction Buneman, Davidson, and Kosky (BDK) algorithm Uses pair-wise correspondences that have “Is-a” and “Has-a” relationships

8. Representation of Models Representation requires (at least) 3 meta-levels Model = database schema, etc Meta-model = type definitions Meta-meta-model = representation language in which models and meta- models are expressed

9. Inputs: Merge (A, B, Map AB ) Two models: A & B Mapping: Map AB First-class models, elements and relationships Mapping elements, origins of additional mapping relationships Non-mapping elements Equality and similarity mapping elements Optional designation of preferred model Optional overrides for Merge behavior

10. Complicated Mapping Non-mapping element Similarity Mapping

11. Mapping Result Result = “a schema that presents all the information of the schemas being merged, but no additional information” Resulting model, G, satisfies Generic Merge Requirements

12. Conflict Resolution Conflicts categorized based on meta- level Representation conflicts Meta-model conflicts Fundamental conflicts

13. Representation Conflicts Occurs when two models describe the same concept in different ways Example, Name represented as ActorName vs. FirstName & LastName Different possible outputs Solutions: Concepts the same based off equality mapping elements Related based of meta-meta-model relationships and elements, FirstName sub element of ActorName Related in more complex fashion beyond meta- meta-model representation, ActorName equals the concatenation of FirstName and LastName

14. Meta-Model Conflicts Merge result violates meta-model- specific constraint SQL table and XML database are merged into a SQL model, there will be no concept of a sub column EnforceConstaints operator requires merge results to conform to a given meta-model.

15. Fundamental Conflicts Meta-meta-model conflicts Merge result violates meta-meta-model rules and cannot be considered a model

16. Fundamental Conflicts Example Meta-meta-model rule: one-type restriction Merge allowed actions: Specify an alternative function to apply for each conflict resolution category Resolve the conflict manually

17. Cardinality Constraints Maximum and minimum occurrences of relations often restricted

18. Acyclicity Models often required to be acyclic Cycles introduced in merging are collapsed into a single element by default User can override default behavior

19. The Merge Algorithm Initialize result G to null Include Elements with equivalence relation Combine element properties Combine and include relationships Fundamental conflict resolution

20. Merge Steps ActorNameActorIDSim Bio Actor BioFirstNameLastName ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc… ID: History: HowRelated: Name: Etc…

21. Contributions Technical requirements for a generic merge operator Use of a first-class input mapping model, enabling richer correspondences Characterization of when Merge can be automatic Taxonomy of conflicts and a definition of conflict resolution strategies Experimental evaluation and results

22. Evaluation Merged Foundational Model of Anatomy (FMA) and GALEN Common Reference Model FMA contains 895,307 elements and 2,032,020 relationships GALEN contains 155,307 elements and 569,384 relationships Significant structural differences Mapping contained 6265 1-to-1 correspondences Evaluation Goals: Limited changes to Merge would be needed Merge would function on models this large The merged results would not be simply read from the mapping (i.e., the conflicts anticipated would occur)

23. Evaluation Few non-fundamental changes had to be made Merging took aprox. 20 hours Merge results 1,045,411 elements with 9,096 duplicates 2,590,969 relationships 338 cycles, most of length 2, where found 1 cycle of length 18 was found Merged correspondences: 3 element merges: 2344 3+ element merges: 623 1 element merges: 1215

24. Conclusions Algorithm is well designed Merge() is implemented in a generic way that allows for different models Definitions of conflict management are given Implementation and execution was very labor intensive Slow, 13 weeks of expert work, 20 hours of processor time Relies on other systems with unknown results

25. Questions?

26. Generic Merge Requirements 1. Element preservation 2. Equality preservation 3. Relationship preservation 4. Similarity preservation 5. Meta-meta-model constraint satisfaction 6. Extraneous item prohibition 7. Property preservation 8. Value preference