On the Complexity of Deriving Schema Mappings from Database Instances
Pierre Senellart1;2 Georg Gottlob3
1
2
3
30 November 2007, Gemo Seminar
Different sources organize the same data differently
Jeffrey D. Ullman
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2007
240 EE Foto N. Afrati, Chen Li, Jeffrey D. Ullman: Using views to generate efficient evaluation plans for queries. J. Comput. Syst. Sci. 73(5): 703724 (2007)
2005
239 EE Jeffrey D. Ullman: Gradiance OnLine Accelerated Learning. ACSC 2005: 36
238 EE Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector GarciaMolina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, HansJörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B.
Zdonik: The Lowell database research selfassessment. Commun. ACM 48(5): 111118 (2005) 237 EE Serge Abiteboul, Richard Hull, Victor Vianu, Sheila A. Greibach, Michael A. Harrison, Ellis Horowitz,
Daniel J. Rosenkrantz, Jeffrey D. Ullman, Moshe Y. Vardi: In memory of Seymour Ginsburg 1928 2004.
SIGMOD Record 34(1): 512 (2005)
2003
236 EE Jeffrey D. Ullman: A Survey of New Directions in Database System. DASFAA 2003: 3
235 EE Jeffrey D. Ullman: Improving the Efficiency of DatabaseSystem Teaching. SIGMOD Conference 2003:
13
234 EE Jim Gray, HansJörg Schek, Michael Stonebraker, Jeffrey D. Ullman: The Lowell Report. SIGMOD Conference 2003: 680
233 EE Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector GarciaMolina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, HansJörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B.
Zdonik: The Lowell Database Research Self Assessment CoRR cs.DB/0310006: (2003)
232 EE Anand Rajaraman, Jeffrey D. Ullman: Querying websites using compact skeletons. J. Comput. Syst. Sci.
66(4): 809851 (2003)
2001
231 EE Chen Li, Mayank Bawa, Jeffrey D. Ullman: Minimizing View Sets without Losing QueryAnswering Power.
ICDT 2001: 99113
230 EE Anand Rajaraman, Jeffrey D. Ullman: Querying Websites Using Compact Skeletons. PODS 2001 229 EE Foto N. Afrati, Chen Li, Jeffrey D. Ullman: Generating Efficient Plans for Queries Using Views. SIGMOD
Conference 2001: 319330
228 EE Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D.
Ullman, Cheng Yang: Finding Interesting Associations without Support Pruning. IEEE Trans. Knowl.
Data Eng. 13(1): 6478 (2001)
2000
227 Hector GarciaMolina, Jeffrey D. Ullman, Jennifer Widom: Database System Implementation PrenticeHall 2000
226 EE Jeffrey D. Ullman: A Survey of AssociationRule Mining. Discovery Science 2000: 114
225 EE Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D.
Ullman, Cheng Yang: Finding Interesting Associations without Support Pruning. ICDE 2000: 489499 224 EE Shinji Fujiwara, Jeffrey D. Ullman, Rajeev Motwani: Dynamic MissCounting Algorithms: Finding
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Motivation
Context
Multiple data sources containing information about similar entities, with someredundancy (e.g., sources of the deep Web).
Several different ways to present this information, i.e., several different schemata.
No a prioriinformation about (some of) these schemata.
How to know the relationshipsbetween these schemata, by just looking at the instances?
Other way to see this problem: Match operator on schema mappings, in the setting ofdata exchange.
Motivation
Context
Multiple data sources containing information about similar entities, with someredundancy (e.g., sources of the deep Web).
Several different ways to present this information, i.e., several different schemata.
No a prioriinformation about (some of) these schemata.
How to know the relationshipsbetween these schemata, by just looking at the instances?
Other way to see this problem: Match operator on schema mappings, in the setting ofdata exchange.
Motivation
Context
Multiple data sources containing information about similar entities, with someredundancy (e.g., sources of the deep Web).
Several different ways to present this information, i.e., several different schemata.
No a prioriinformation about (some of) these schemata.
How to know the relationshipsbetween these schemata, by just looking at the instances?
Other way to see this problem: Match operator on schema mappings, in the setting ofdata exchange.
Problem definition
Problem
Given two (relational) database instances I and J with different schemata, what is the optimal description of J knowing I (with a finite set of formulas in some logical language)?
What does optimal implies:
Conciseness of description.
Validity of facts predicted byI and . All facts ofJ explainedby I and .
(Note the asymmetry between I and J; context ofdata exchange where J is computed fromI and ).
Problem definition
Problem
Given two (relational) database instances I and J with different schemata, what is the optimal description of J knowing I (with a finite set of formulas in some logical language)?
What does optimal implies:
Conciseness of description.
Validity of facts predicted byI and . All facts ofJ explainedby I and .
(Note the asymmetry between I and J; context ofdata exchange where J is computed fromI and ).
Outline
Source-to-target tuple-generating dependencies
Definition (Source-to-target tgd) First-order formula of the form:
8x'(x) ! 9y (x;y) with:
'conjunctionof source relation atoms;
conjunctionof target relation atoms;
all variables ofx bound in'.
Example
8x18x2R1(x1;x2) ^R2(x2) ! 9y R0(x1;y)
Particular tgds
We consider two ways of having simplertgds:
Disallow existential quantifiers on the right hand-side: full tgds.
Disallow cycles on both left- and right-hand sides: acyclic tgds.
(Classical notion of acyclicity on hypergraphs extending the basic notion of acyclicity on graphs.)
Examples
8x18x28x3R1(x1;x2) ^R2(x2;x3) ^R3(x3;x1) !R0(x1)iscyclic(and full).
8x18x28x3R1(x1;x2) ^R2(x2;x3) !R0(x1)isacyclic(and full).
We then consider the languages:
Ltgd: arbitrary source-to-target tgds;
Lfull: full tgds;
Lacyc: acyclic tgds;
Lfacyc: full and acyclic tgds.
Particular tgds
We consider two ways of having simplertgds:
Disallow existential quantifiers on the right hand-side: full tgds.
Disallow cycles on both left- and right-hand sides: acyclic tgds.
(Classical notion of acyclicity on hypergraphs extending the basic notion of acyclicity on graphs.)
Examples
8x18x28x3R1(x1;x2) ^R2(x2;x3) ^R3(x3;x1) !R0(x1)iscyclic(and full).
8x18x28x3R1(x1;x2) ^R2(x2;x3) !R0(x1)isacyclic(and full).
We then consider the languages:
Ltgd: arbitrary source-to-target tgds;
Lfull: full tgds;
Lacyc: acyclic tgds;
Lfacyc: full and acyclic tgds.
Particular tgds
We consider two ways of having simplertgds:
Disallow existential quantifiers on the right hand-side: full tgds.
Disallow cycles on both left- and right-hand sides: acyclic tgds.
(Classical notion of acyclicity on hypergraphs extending the basic notion of acyclicity on graphs.)
Examples
8x18x28x3R1(x1;x2) ^R2(x2;x3) ^R3(x3;x1) !R0(x1)iscyclic(and full).
8x18x28x3R1(x1;x2) ^R2(x2;x3) !R0(x1)isacyclic(and full).
We then consider the languages:
Ltgd: arbitrary source-to-target tgds;
Lfull: full tgds;
Lacyc: acyclic tgds;
Lfacyc: full and acyclic tgds.
How to define the pertinence of a set of tgds?
Example
R R0
a b c d
a a b b c a d d g h 0 = ?
1 = f8x R(x) !R0(x;x)g 2 = f8x R(x) ! 9y R0(x;y)g 3 = f8x8y R(x) ^R(y) !R0(x;y)g 4 = f9x9y R0(x;y)g
How to define the pertinence of a set of tgds?
Example
R R0
a b c d
a a b b c a d d g h 0 = ?
1 = f8x R(x) !R0(x;x)g 2 = f8x R(x) ! 9y R0(x;y)g 3 = f8x8y R(x) ^R(y) !R0(x;y)g 4 = f9x9y R0(x;y)g
Idea
Size of a formula: number of occurrences of variables and constants.
Costof a schema mapping : Size of theminimum repairof that is valid and explains all facts of J.
Types of repairs considered:
“fix” a universal quantifier by adding conditions (x =a or x 6=a);
“fix” an existential quantifier by giving corresponding constants ((x) !y=a with a conjunction of conditions on universally quantified variables);
addground factsto the target instance.
The problem is then to find a schema mapping ofminimal cost.
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x) !R0(x;x)
Cost: 17
PredictedR0 a a b b c c d d
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x)^x 6=c !R0(x;x)
Cost: 17
PredictedR0 a a b b d d
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x)^x 6=c !R0(x;x)
R(c;a)
R(g;h) Cost: 17
PredictedR0 a a b b c a d d
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x)^x 6=c !R0(x;x)
R(c;a) R(g;h)
Cost: 17
PredictedR0 a a b b c c d d g h
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x)^x 6=c !R0(x;x)
9x9y R(x;y) ^x =c^y =a 9x9y R(x;y) ^x =g^y=h
Cost: 17
PredictedR0 a a b b c c d d g h
Example of cost computation
Example
R R0
a b c d
a a b b c a d d g h 8x R(x) ^x 6=c !R0(x;x)
9x9y R(x;y) ^x =c^y =a 9x9y R(x;y) ^x =g^y=h
Cost: 17
PredictedR0 a a b b c c d d g h
Problems considered
Decision problemsof interest:
Cost : Is the cost of a given schema mapping less thanK? Optimality : Is a given schema mapping optimal?
Complexity? Algorithms?
Problems considered
Decision problemsof interest:
Cost : Is the cost of a given schema mapping less thanK? Optimality : Is a given schema mapping optimal?
Complexity? Algorithms?
Outline
Behavior for simple operators
Consider the elementary operatorsof the relational algebra:
Projection Intersection
Selection (conjunction of atomic conditions) Cross Product
Join (on a given attribute) Theorem
For any elementary operator , the tgd naturally associated with is optimal with respect to (I; (I)) (or ((J);J)), under some basic assumptions.
Behavior for simple operators
Consider the elementary operatorsof the relational algebra:
Projection Intersection
Selection (conjunction of atomic conditions) Cross Product
Join (on a given attribute) Theorem
For any elementary operator , the tgd naturally associated with is optimal with respect to (I; (I)) (or ((J);J)), under some basic assumptions.
Examples of naturally associated tgds
Examples
Condition I andJ Optimal tgd
Projection
I 6= ? J= 1(I) R(x;y) !R0(x) 1(J) \ 2(J) = ?,
j1(J)j >2 I = 1(J) R(x) ! 9y R0(x;y) Selection j'(I)j >size(')+3 2 J= '(I) R(x) !R0(x)
'(J) 6= ? I = '(J) R(x) !R0(x) Product R1I6= ?,RI26= ? J=RI1RI2 R1(x) ^R2(y) !R0(x;y)
R01
J6= ?,R02
J 6= ? I =R10 JR02
J R(x;y) !R01(x) ^R20(y)
(Combined) Complexity Results
Ltgd Lfull
Cost P3,P2-hard P2, (co)NP-hard Optimality P4, (co)NP-hard P3, (co)NP-hard
Lacyc Lfacyc
Cost P2, (co)NP-hard NP-complete Optimality P3, (co)NP-hard P2, (co)NP-hard
(Combined) Complexity Results
Ltgd Lfull
Cost P3,P2-hard P2, (co)NP-hard Optimality P4, (co)NP-hard P3, (co)NP-hard
Lacyc Lfacyc
Cost P2, (co)NP-hard NP-complete Optimality P3, (co)NP-hard P2, (co)NP-hard
(Combined) Complexity Results
Ltgd Lfull
Cost P3,P2-hard P2, (co)NP-hard Optimality P4, (co)NP-hard P3, (co)NP-hard
Lacyc Lfacyc
Cost P2, (co)NP-hard NP-complete Optimality P3, (co)NP-hard P2, (co)NP-hard
(Combined) Complexity Results
Ltgd Lfull
Cost P3,P2-hard P2, (co)NP-hard Optimality P4, (co)NP-hard P3, (co)NP-hard
Lacyc Lfacyc
Cost P2, (co)NP-hard NP-complete Optimality P3, (co)NP-hard P2, (co)NP-hard
(Combined) Complexity Results
Ltgd Lfull
Cost P3,P2-hard P2, (co)NP-hard Optimality P4, (co)NP-hard P3, (co)NP-hard
Lacyc Lfacyc
Cost P2, (co)NP-hard NP-complete Optimality P3, (co)NP-hard P2, (co)NP-hard
Vertex-Cover in r -partite r -uniform hypergraph
Vertex-Cover: find a set of vertices of minimal size that cover all (hyper)edges in a (hyper)graph.
NP-complete for general (hyper)graphs.
PTIME for bipartite graphs (Kőnig’s theorem).
Lemma
Vertex-Cover is NP-complete for r -partite r -uniform hypergraphs for r >3.
r-partite: partition of the set of vertices intor sets, with no hyperedge spanning vertices of two different sets.
r-uniform: every hyperedge spansr vertices.
Encoding of 3-SAT
x1
x1
x2
x2
x3
x3
y1
y1
y2
y2
y3
y3
z1
z1 z2
z2
z3
z3
:z _x _y
Cost is NP-hard for L
facycReduction from Vertex-Cover in 3-partite 3-uniform hypergraphs.
Withoutx =a repairs on the left-hand side of a tgd:
R(x1;x2;x3) !R0(x1) Source instance: hypergraph Target instance: empty
Cost: size of the tgd plus twice the minimum size of a vertex cover.
With x =a repairs: a little more difficult, but feasible!
Cost is NP-hard for L
facycReduction from Vertex-Cover in 3-partite 3-uniform hypergraphs.
Withoutx =a repairs on the left-hand side of a tgd:
R(x1;x2;x3) !R0(x1) Source instance: hypergraph Target instance: empty
Cost: size of the tgd plus twice the minimum size of a vertex cover.
With x =a repairs: a little more difficult, but feasible!
Cost is NP-hard for L
facycReduction from Vertex-Cover in 3-partite 3-uniform hypergraphs.
Withoutx =a repairs on the left-hand side of a tgd:
R(x1;x2;x3) !R0(x1) Source instance: hypergraph Target instance: empty
Cost: size of the tgd plus twice the minimum size of a vertex cover.
With x =a repairs: a little more difficult, but feasible!
Cost is NP-hard for L
facycReduction from Vertex-Cover in 3-partite 3-uniform hypergraphs.
Withoutx =a repairs on the left-hand side of a tgd:
R(x1;x2;x3) !R0(x1) Source instance: hypergraph Target instance: empty
Cost: size of the tgd plus twice the minimum size of a vertex cover.
With x =a repairs: a little more difficult, but feasible!
Outline
Extension to Relational Calculus
Definition of repairs can be extended to relational calculus.
Same definition of cost, optimality.
Costis not recursive (but co-r.e.).
Computability ofOptimality: open(!).
Other Cost Functions
Why not counting the number of tuples to add or remove in J? . . . because it can beexponential in the size of the schema mapping!
Why not counting the number of tuples to add or remove in I orJ? . . . because selections are not captured!
Other Cost Functions
Why not counting the number of tuples to add or remove in J? . . . because it can beexponential in the size of the schema mapping!
Why not counting the number of tuples to add or remove in I orJ? . . . because selections are not captured!
Other Cost Functions
Why not counting the number of tuples to add or remove in J? . . . because it can beexponential in the size of the schema mapping!
Why not counting the number of tuples to add or remove in I orJ? . . . because selections are not captured!
Other Cost Functions
Why not counting the number of tuples to add or remove in J? . . . because it can beexponential in the size of the schema mapping!
Why not counting the number of tuples to add or remove in I orJ? . . . because selections are not captured!
Outline
In summary...
Formal framework for the discovery ofsymbolic relations between two data sources.
High complexity(up to fourth level of PH).
Link with Inductive Logic Programming?
Heuristics?
Generalization of acyclicity?
In summary...
Formal framework for the discovery ofsymbolic relations between two data sources.
High complexity(up to fourth level of PH).
Link with Inductive Logic Programming?
Heuristics?
Generalization of acyclicity?