1. A scalable algorithm for answering queries using views Rachel Pottinger, Alon Levy [2000] Rachel Pottinger and Alon Y. Levy A Scalable Algorithm for Answering Queries Using Views. In Proceedings of VLDB 2000. Presented by: Paea LePendu paea@cs.uoregon.edu

2. Introduction Θ Q(x) :- cites(x,y), cites(y,x), SameTopic(x,y) ● This looks like a RULE (HEAD :- Body) ● Join Predicates / Database relations Θ But this paper is about VIEWS. Θ SQL ↔ Views ↔ Rules Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work SQL ↔ Views ↔ Rules

3. Outline Θ Introduction Θ Motivation Θ Contributions Θ Algorithms Θ Experimental results Θ Future work Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work SQL ↔ Views ↔ Rules Bucket, MiniCon

4. What is a view (in SQL)? Θ Virtual Table Θ Logical data Θ Query vs. View Θ Materialized View Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work SQL ↔ Views ↔ Rules

5. Logical Online Instructor Evaluation System for Whitman College, by Paea LePendu, 2003. Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

6. Virtual Table Online Instructor Evaluation System for Whitman College, by Paea LePendu, 2003. Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

7. Re-cap Θ SQL ↔ Views ↔ Rules Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

8. Datalog Θ Deductive Databases Θ Facts, rules, queries ● Forward Chaining ● Backward Chaining Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work SQL ↔ Views ↔ Rules Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems (3rd Edition), Addison-Wesley, 2000.

9. Facts, Rules, Queries * Θ superior(james,joyce)? :- supervise(X,Z), superior(Z,Y) (bindings: X→ james, Z → franklin, Y→joyce) Ramez Elmasri and Shamkant B. Navathe, Fundamentals of Database Systems (3 rd Edition), Addison-Wesley, 2000. Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

10. Re-cap Θ Q(x) :- cites(x,y), cites(y,x), SameTopic(x,y) Θ V4(a) :- cites(a,b), cites(b,a) Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work SQL ↔ Views ↔ Rules

11. Why rewrite a query using a view? Θ Data management Θ Data integration Θ Query optimization Θ Data independence Θ Materialized Views Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

12. Data Integration Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Maximally-contained rewriting Conjunctive query Materialized views

13. Contributions Θ MiniCon Θ Algorithm analysis ● Bucket ● Inverse Rules ● MiniCon Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

14. Q :- V 1, V 2,..., V n Θ NP complete Θ Bucket algorithm Θ Inverse rules algorithm Θ MiniCon algorithm Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p487.

15. Bucket Θ Redundant rewritings Θ Cartesian product Θ Inefficient Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p487.

16. Bucket Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p487.

17. Q :- V 1, V 2,..., V n Θ Bucket algorithm Θ Inverse rules algorithm Θ MiniCon algorithm Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

18. Inverse Rules Θ Polynomial time Θ Redundant rewritings Θ Redeemable ● Back to Bucket Pottinger et al. 2000 p488. Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

19. Q :- V 1, V 2,..., V n Θ Bucket algorithm Θ Inverse rules algorithm Θ MiniCon algorithm Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

20. MiniCon Θ Bucket Θ Change perspective Θ MCD Θ Eliminates redundancy Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

21. MCD Θ Partial mappings from Vars(Q) to Vars(V) ● Constraint: distinguished range contained in head variables Θ Head homomorphism mappings Θ Covered subgoals ● Constraint: ranged existential variables are distinguished Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p489.

22. MCD V6(f,h)? 123 y→g? Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p489. V4(a) | a→a | x→a, y→b | 1,2 (3) eliminated

23. combineMCD Θ Pair-wise disjoint subsets of subgoals ● Define mappings Q↔V ● Create conjunctive rewriting Θ Union resulting conjunctive rewritings Q1'(x) :- V6(x,x) Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p490. V5(c,d) | c→c, d→d | x→c, y→d | 3 (1,2) eliminated V6(f,f) | f→f, h→f | x→f, y→f | 1,2,3

24. Experimental Results Θ Chain, Star, Complete Queries Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work (a) Steinbrunn, G. Moerkotte, and A. Kemper. Heuristic and randomized optimization for the join. VLDB Journal, 6(3):191-208, 1997. (b) W. Sun and C. Yu. Semantic Query Optimization for Tree and Chain Queries. IEEE Transactions on Knowledge and Data Engineering. 6(1):136-15, 1994. (c) http://www.eecs.umich.edu/db/mbench/images/chainTwig.ppt

25. Chain Queries Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p492.

26. Star and Complete Queries Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work Pottinger et al. 2000 p493.

27. Future Work Θ Query optimization Θ Semantic Search ? Introduction → Motivation → Contributions → Algorithms → Expmt'l Results → Future Work

28. Questions for you Θ Why the distinction between views and materialized views? Θ Is a maximally-contained rewriting satisfactory? Θ What similarities do you see with the Abiteboul et al paper (Correspondence and translation for heterogeneous data)?