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Bidirectional Mappings for Data and Update Exchange

Bidirectional Mappings for Data and Update Exchange. Grigoris Karvounarakis Zachary G. Ives University of Pennsylvania. WebDB 2008. Collaborative Data Sharing Systems [CIDR05]. ∆ B +/−. Peer B. ∆ C +/−. Peer C. Peer A. DBMS. ∆ A +/−. ∆ A +/−. Queries, edits. PUBLISH.

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Bidirectional Mappings for Data and Update Exchange

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  1. BidirectionalMappings for Data and Update Exchange Grigoris Karvounarakis Zachary G. Ives University of Pennsylvania WebDB 2008

  2. Collaborative Data Sharing Systems [CIDR05] ∆B+/− Peer B ∆C+/− Peer C Peer A DBMS ∆A+/− ∆A+/− Queries, edits PUBLISH Each peer has a local instance Relate peers by schema mappings Support update exchange according to different administrator policies

  3. Unidirectional update exchange • ORCHESTRA [SIGMOD06,VLDB07]: • Each mapping (tgd[Fagin+03]/GLAV[Lenzerini02]) has source and target peers • Propagate effects of updates forward, to target • Related to view maintenance m1: R(xy) Æ S(yz) ! T(xyz) m2: T(xyz) !U(xyz) RR + (1,2) TT UU (1,2) (1,2) m2 m1 – SS (1,3) (1,3) + m2 + (2,3)

  4. Bidirectionalmappings and update exchange • Goal: Tighter coupling between peers • Both forward and backward propagation of updates (insertions/deletions) along mappings • E.g., for mirroring of content between peers • Our contributions: • Language for specifying bidirectional mappings and update policies (for backwards propagation of deletions) • Algorithms for propagation of updates in both directions and for propagation with detection and prevention ofside effects [Dayal+82] atrun-time • Experimental evaluation, illustrating feasibility

  5. m1 :R(xy)ÆS(xzw)!T(xyz)ÆV(wx) m1:T(xyz)ÆV(wx)!R(xy)ÆS(xzw) m2: T(xyz) ! U(xyz) m2 :U(xyz) ! T(xyz) Bidirectional update exchange: insertions m1:R(xy)ÆS(xzw)$T(xyz)ÆV(wx) m2:T(xyz)$U(xyz) R T U (1, 1) + (1, 1, 1) (1, 1, 1) m2 m1 (3, 2) + (1, 1, 2) (1, 1, 2) m2 S (3, 2, 3) (3, 2, 3) + m2 m1 (1, 1, 4) + V (1, 2, 4) + m1 (4,1) (3, 3, 5) (5,3) +

  6. Propagating deletions to source tuples • Need to track down and delete source tuples from which deleted ones were derived • Multiple options for propagating updates backwards over joins • User specified update policies resolve this ambiguity: • Guaranteed to perform any deletion, as long as there is at least one * in each side * * R(xy) Æ S(xzw) $ T(xyz)ÆV(wx)

  7. Bidirectional update exchange: deletions m1:*R(xy)ÆS(xzw)$T(xyz)Æ*V(wx) m2:*T(xyz)$*U(xyz) R T U (1, 1) + (1, 1, 1) (1, 1, 1) m2 m1 (3, 2) + (1, 1, 2) (1, 1, 2) (1, 1, 2) (1, 1, 2) m2 S (3, 2, 3) (3, 2, 3) + m2 m1 (1, 1, 4) + V (1, 2, 4) + m1 (4,1) (3, 3, 5) (3, 3, 5) (5,3) +

  8. Avoiding side effects on T m1:*R(xy)ÆS(xzw)$T(xyz)Æ*V(wx) m2:*T(xyz)$*U(xyz) R T U (1, 1) + (1, 1, 1) (1, 1, 1) m2 m1 (3, 2) + (1, 1, 2) (1, 1, 2) m2 S (3, 2, 3) (3, 2, 3) + m2 m1 (1, 1, 4) + V (1, 2, 4) + m1 (4,1) (3, 3, 5) (5,3) +

  9. Experimental evaluation • Implementation strategy: Mappings -> Datalog programs -> SQL + fixpoint • Java layer over RDBMS (DB2) • Synthetic update workload sampled from SWISS-PROT biological data set • Randomly-generated schemas and mappings • 2000 initial tuples in each peer before propagation • Questions: • Overhead of bidirectional over unidirectional (in paper) • Feasibility of deletion propagation and overhead of side effect detection

  10. Propagation of 10% dels is feasible for 5 peers

  11. Related work • Data exchange[Haas+99, Miller+00, Popa+02, Fagin+03, Hernich+07], peer data exchange [Fuxman+05] • View update[Dayal+82, Bancilhon+81], Harmony [Bohannon+06], [Matsuda+07] • Incremental view maintenance[Gupta+93] • Peer data management systems (PDMS) Piazza[Halevy+03,04],Hyperion[Kementsietsidis+04], [Bernstein+02], [Calvanese+04], ...

  12. Conclusions and future work • Contributions: • Bidirectional mappings for update exchange between peers that want closer collaboration • Combine forward and backward propagation to compute peer instances incrementally • Incorporate update policies for backward propagation • Dynamic detection and prevention of side effects • Future work: • Combine unidirectional and bidirectional mappings, to also guarantee that all initial deletions are performed • Study opportunities for performance optimization

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