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Computing Capabilities of Mediators

Computing Capabilities of Mediators. Ramana Yerneni Chen Li Hector Garcia-Molina Jeffrey Ullman. T alk created by Rahul, modified by Sudarshan. Wrapper Architecture. User Query. Catalog. Mediator. Wrapper. Wrapper. Data Source. Data Source. Data Source. Data Source. Scope.

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Computing Capabilities of Mediators

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  1. Computing Capabilities of Mediators Ramana Yerneni Chen Li Hector Garcia-Molina Jeffrey Ullman Talk created by Rahul, modified by Sudarshan

  2. Wrapper Architecture User Query Catalog Mediator Wrapper Wrapper Data Source Data Source Data Source Data Source

  3. Scope • Framework for expressing source capabilities • Approach for computing mediator capabilities • Extending mediator capabilities

  4. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description

  5. Capabilities of Data Sources • Attribute Adornments: • Adornment f : for free • Adornment u : for unspecifiable • Adornment b : for bound • Adornment c[s] : for compulsory constant • Adornment o[s] : for optional constant

  6. Capabilities of Data Sources R ( x1, Y, Z ) R ( X, Y, Z ) R(X Y Z) {b u f } R(X Y Z) {u b f } Exported Views Data Source

  7. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description • Example

  8. Simple Mediators • Simple Mediator Properties • No post processing • Performs joins blindly • Union Views • Variable Adornments in Union View are computed from base view adornments • Mapping Function

  9. Simple Mediators Mapping Function:

  10. Simple Mediators • Union of two views • Example R(X, Y, Z) { b b u } Mediator Union View R(X, Y, Z) {b f u} Data Source R(X, Y, Z) {f b u} Data Source

  11. Simple Mediators • Union of two base views with multiple templates • Cross product of two template sets • For each pair apply mapping function • Union of multiple base views with multiple templates • Process two base views at a time • Repeat (n-1) times

  12. Simple Mediators • Join Views • Non-join attribute adornments are copied as they are in join view • Join attributes are computed using mapping function R1R2(X, Y, Z) { b b u } Mediator Join View R1(X,Y) {b f } Data Source R2(Y,Z) {b u} Data Source

  13. Simple Mediators • Selection Views • Pass the query to underlying data source as it is if it fits the template • Filter the results according to selection predicates • Projection Views • Hidden attributes are left unspecified • Works only if hidden attribute adornments are f, u, o

  14. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description

  15. Advance Techniques in Mediators • Union Views • Post processing • Filter the input query to fit source views • Post process the results to match user requirements • Union view adornments are computed using Mapping function

  16. Advance Techniques in Mediators • Union View Example R(x1,y1,z1) R(X, Y, Z) { b u u } Mediator Union View { b f f } R(x1,Y,z1) R(x1,y1,Z) R(X,Y,Z){b f u } Data Source R(X,Y,Z){b u f } Data Source

  17. Union with postprocesing Mapping function for union with postprocessing

  18. Joins with Binding Passing • Join Views • Non-join attribute adornments are copied as they are • Passing join attribute bindings from left to right view • Join attribute adornments are computed using new mapping function

  19. Joins with Binding Passing: Example • Join views: passing bindings R(x1,Z,V) R(X, Z, V) { b b f } Mediator Join View { b f f } R(z1,V) R(x1,Z) R1(X,Z){b f } Data Source R2(Z,V){b f } Data Source

  20. Join with Binding Passing + Postprocessing • Aka “Dependent join” • Mapping function (left relation -> rows) Rt. Left

  21. Multiple Joins/Bindings • Join of 2 relations with multiple bindings for each relation • Cross product • one result template for each combination • Ordered sequence of n relations • Compute pairwise from left • Binding pattern depends on join order • n! possible join sequences with n relations • Compute pattern (pairwise) for each sequence, take union • Mediator will have to select appropriate join order depending on query bindings

  22. Selections with postprocessing Selection may or may not provide value for attribute

  23. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description

  24. Dynamic Mediators • Liberal templates • Answerability depends on state of data in data source • Can’t be always answered • Conservative templates • Can always be answered • Computed using techniques discussed in earlier slides

  25. Dynamic Mediators • Liberal Template Example R(x1,Y,Z) R(X, Y, Z) { b c[s] f } Mediator Join View { b f f } R(y1,Z) R1(x1,Y) R1(X,Y){b f } Data Source R2(Y,Z){c[s] f } Data Source

  26. Liberal Template for Join Views Rt. Left

  27. Dynamic Mediators • Simple Mediators • Mediators employing advanced techniques • Post Processing • Passing bindings • Dynamic mediators • Liberal Templates

  28. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description

  29. Concise Capability Description • Mediator view space is exponential • k templates per view, n sources • View space will be kn • Large number of templates are redundant • M1(b f b ) and M2(f f f ) : M2 is redundant

  30. Concise Capability Description • Adornment Graph f b O[s2] C[s1] u

  31. Concise Capability Description • Subsumption test • Template T subsumes T1 iff Every attribute x in T1 is atleast as restrictive as T • Subsumption test partitions the set of templates • Select the template which subsumes all other templates in a partition

  32. Pathway • Capabilities of data sources • Simple mediators • Advanced techniques in mediators • Dynamic mediators • Concise capability description

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