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CS 245: Database System Principles Notes 14: Coping with Limited Capabilities of Sources

CS 245: Database System Principles Notes 14: Coping with Limited Capabilities of Sources. Hector Garcia-Molina. Heterogeneous Databases. Distributed Database System. DBMS 1. DBMS 2. legacy. web site. data. data. data. data. Limited Capabilities. Example: Amazon.com.

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CS 245: Database System Principles Notes 14: Coping with Limited Capabilities of Sources

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  1. CS 245: Database System PrinciplesNotes 14: Coping with LimitedCapabilities of Sources Hector Garcia-Molina Notes 14

  2. Heterogeneous Databases Distributed Database System DBMS1 DBMS2 legacy web site data data data data Notes 14

  3. Limited Capabilities Notes 14

  4. Example: Amazon.com must specify at least one of these author: title: this attribute not returned subject: format: menu of choices price: cannot query on this attribute Notes 14

  5. Example: BarnesAndNoble.com must specify at least one of these author: title: Menu of choices subject: format: can query if one of other attributes specified price: Notes 14

  6. Why Limited Capabilities? • Search forms • Security • Indexes • Legacy Notes 14

  7. Capability vs. Content • Capability description • Can only search for subject = “art,” “history,” “science” • Content description • Source only contains subject = “art,” “history,” “science” Notes 14

  8. Outline • Describing source capabilities • Extending source capabilities • How mediators cope with limited capabilities • Mediator capabilities • Other topics mediator source source source Notes 14

  9. Describing Query Capabilities R(X, Y, ... Z) • Adornments: • f: may or may not specify • u: cannot be specified • b: must be specified • c[S]: specified from list S • o[S]: optional, chose from S Notes 14

  10. Describing Query Capabilities R(X, Y, ... Z) • With output restriction • f’ • u’ • b’ • c’[S] • o’[S] • Adornments: • f: may or may not specify • u: cannot be specified • b: must be specified • c[S]: specified from list S • o[S]: optional, chose from S Notes 14

  11. Example • Relation R(X, Y, Z) • Description Templates: bu’f, uf’c[z1, z2] • Answerable queries: R(x1, Y, Z), R(X, Y, z1) • Unanswerable queries: R(X, y1, Z), R(X, Y, z3) Notes 14

  12. Other Description Mechanisms • Tsimmis • query templates • Information Manifold • capability records (# bound attrs, conditions ok,...) • Disco • Garlic • black box • Contex-free grammars Notes 14

  13. Extending Source Capabilities Query: author=“Freud” AND price > 10 wrapper amazon Source: R(author, price, ...) Template: b, u, ... Notes 14

  14. Extending Source Capabilities Query: author=“Freud” AND price > 10 Wrapper Filter: price > 10 wrapper Source Query: author=“Freud” amazon Source: R(author, price, ...) Template: b, u, ... Notes 14

  15. Another Example Query: (author = “Freud” OR author = “Jung”) AND price < 10 wrapper Barnes&Noble R(author, price, ...) No disjunctive conditions; Price can only be specified with author Notes 14

  16. Another Example Query: (author = “Freud” OR author = “Jung”) AND price < 10 Union Operation wrapper Q1: author = “Freud” AND price < 10 Q2: author = “Jung” AND price < 10 Barnes&Noble R(author, price, …) No disjunctive conditions; Price can only be specified with author Notes 14

  17. Extending Source Capabilities • General scheme: • try many query rewritings • check if query fragments supported by source • check if wrapper can combine answer fragments • do all this very efficiently!! [See ICDE99 paper] • Tsimmis, Info Manifold: no disjunctive queries • DISCO: no query splitting • Garlic: only CNF queries Notes 14

  18. Mediator Processing Query: M(5, Y, Z, W, 3) M(X, Y, Z, W, U) = Join(R, T) mediator source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b Notes 14

  19. Plan 1 Query: M(5, Y, Z, W, 3) (3) Join answers M(X, Y, Z, W, U) = Join(R, T) mediator (1) R(5, Y, Z) (2) T(Z, W, 3) source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b Notes 14

  20. Plan 2 Query: M(5, Y, Z, W, 3) (3) Join answers (2) for each (z,w,u)  P: R(5, Y, u) M(X, Y, Z, W, U) = Join(R, T) mediator (1) P = T(Z, W, 3) source source T(Z, W, U) f, u, b R(X, Y, Z) f, f, b Notes 14

  21. Mediator Plan Generation • Need feasible and efficient plan • Search space is huge • Tsimmis, Info Manifold, Garlic: • exponential algorithms • Polynomial algorithms: • often find optimal or near-optimal plan • bounded performance • [See ICDT99 Paper] Notes 14

  22. Conclusion • Not all sources are created equal! • Need to • describe what sources can do • efficiently process queries with limited sources • describe what mediators can do • exploit content information • deal with unavailable sources Notes 14

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