Optimal Query Processing Meets Information Theory PowerPoint Presentation

Optimal Query Processing Meets Information Theory PowerPoint Presentation

2018-10-06 8K 8 0 0

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Dan Suciu – University of Washington. Hung Ngo. Mahmoud Abo-Khamis . [PODS’2016]. [PODS’2017]. – . RelationalAI. Inc.. Basic Question. What is the optimal runtime to compute a query . Q. on a database . ID: 685331

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Presentations text content in Optimal Query Processing Meets Information Theory

Slide1

Optimal Query Processing Meets Information Theory

Dan Suciu – University of Washington

Hung Ngo

Mahmoud Abo-Khamis

[PODS’2016]

[PODS’2017]

RelationalAI

Inc.

Slide2

Basic Question

What is the optimal runtime to compute a query Q on a database

D?Q, D are labeled hypergraphsProblem 1: list all occurrences in

Q in DProblem 2: check if there exists

Q in DData complexity: Q is fixed, runtime = f(

D)2

Slide3

Example Queries

3

Enumerate all labeled triangles:

R(X,Y)

S(Y,Z) ∧ T(Z,X)

∃x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

Check if there exists a labeled 4-cycle

U

Z

X

Y

Z

X

Y

R

R

S

S

T

T

K

Slide4

Main Results

Problem 1: enumeration problem

Thm

D,(1) |Q(D)| ≤ Entropic-bound ≤ Polymatroid-bound

(2) Q(D) computable in time Õ(Polymatroid-bound)

Fix statistics for

D

(cardinalities, functional dependencies, max degrees)

Fix the query

Q

Problem 2: decision problem

Thm

D

,

Q(

D) is computable in time Õ(2submodular-width)

Tight, but openif computable

Computable,

but not tight

Optimal?

Slide5

Main Principle

Find information-theoretic

proof ofthe upper bound, or the submodular widthConvert proof

to algorithm

5

Slide6

Outline

Enumeration problemDecision problemConclusions

6

Slide7

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q(D)| ≤ N

2S.Y is a key: |Q(D)| ≤ NS.Y has degree ≤ d: |Q(D)| ≤

d×NE.g. R(X,Y)

S(Y,Z)

T(Z,X)

No other info: |Q(D)| ≤ N

3/2

7

Slide8

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |Q(D)| ≤ NS.Y has degree ≤ d:

|Q(D)| ≤ d×N

E.g. R(X,Y)

S(Y,Z)

T(Z,X)

No other info: |Q(D)| ≤ N

3/2

8

Slide9

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |Q(D)| ≤ N

S.Y has degree ≤ d: |Q(D)| ≤ d×N

E.g. R(X,Y)

S(Y,Z)

T(Z,X)

No other info: |Q(D)| ≤ N

3/2

9

Slide10

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |

Q(D)| ≤ NS.Y has degree ≤ d:

|Q(D)| ≤

d×N

E.g. R(X,Y)

S(Y,Z)

T(Z,X)

No other info: |Q(D)| ≤ N

3/2

10

Slide11

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |

Q(D)| ≤ NS.Y has degree ≤

d

:

|Q(D)| ≤

d×N

E.g. R(X,Y)

S(Y,Z)

T(Z,X)

No other info: |Q(D)| ≤ N

3/211

Slide12

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |

Q(D)| ≤ NS.Y has degree ≤

d

: |

Q

(

D

)| ≤

d

×

NE.g. R(X,Y)

∧ S(Y,Z) ∧

T(Z,X) No other info: |Q(D)| ≤ N3/2

12

Slide13

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |

Q(D)| ≤ NS.Y has degree ≤

d

: |

Q

(

D

)| ≤

d

×

NE.g. R(X,Y)

∧ S(Y,Z) ∧

T(Z,X) No other info: |Q(D)| ≤ N3/2

13

Slide14

Maximum Output Size

maxD satisfies stats (|

Q(D)|)E.g. R(X,Y)

∧ S(Y,Z), |R|, |

S| ≤ NNo other info: |Q

(D)| ≤ N2S.Y is a key: |

Q(D)| ≤ NS.Y has degree ≤

d

: |

Q

(

D

)| ≤

d

×

NE.g. R(X,Y)

∧ S(Y,Z) ∧

T(Z,X) No other info: |Q(D)| ≤ N3/2

14

Slide15

Background: Entropy, Polymatroid

Fix a set

X={X

1,…,Xk} and a function H: 2

X  R+

Def

H is called entropic if there exists randomvariables

X

s.t.

H(

U

) = entropy of

U, for U ⊆ X

Def

H is a polymatroid ifH(

∅) = 0H(V) ≥ H(U) for

U ⊆ VH(U

) + H(V) ≥ H(U ∩

V) + H(U ∪ V

)

Every entropic function is a polymatroidConverse fails for k≥4 [Zhang&Yeung’98]

Shannon

inequalities

Slide16

Enumeration Problem

16

Theorem ∀D

that satisfies the statisticslog |Q(D)| ≤ max

H entropic satisfying stats H(X) ≤ max H polymatroid satisfying stats

H(X)

Thm ∀

D

,

Q

(

D

) computable in time

Õ

(

Polymatroid-bound)

Fix a set of statistics for D (cardinalities, FDs, degrees)Fix a query

Q with variables X={X1,…,

Xk}

Asymptotically tight,but open if computable

Computable

in EXPTIME, but not tight

Slide17

Proof of Upper Bound

17

Q(X,Y,Z) = R(X,Y)

∧ S(Y,Z)

∧ T(Z,X)Database D

 entropic function H

Z

X

Y

Slide18

Proof of Upper Bound

X

Y

a

3

a

2

b

2

d

3

Y

Z

3

m

2

q

3

q

2

m

Z

X

m

a

q

a

q

b

m

d

R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

18

Q(X,Y,Z) = R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

entropic function H

Z

X

Y

Slide19

Proof of Upper Bound

X

Y

Z

a

3

m

a

2

q

b

2

q

d

3

m

a

3

q

X

Y

a

3

a

2

b

2

d

3

Y

Z

3

m

2

q

3

q

2

m

Z

X

m

a

q

a

q

b

m

d

Output

Q(

D

)

R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

19

Q(X,Y,Z) = R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

entropic function H

Z

X

Y

Slide20

Proof of Upper Bound

X

Y

Z

a

3

m

1/5

a

2

q

1/5

b

2

q

1/5

d

3

m

1/5

a

3

q

1/5

X

Y

a

3

a2

b

2

d3

Y

Z

3

m

2

q

3

q

2

m

Z

X

m

a

q

a

q

b

m

d

Output

Q(

D

)

R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

20

H(

XYZ

) = log |

Q

(

D

)|

Q(X,Y,Z) = R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

entropic function H

Z

X

Y

Slide21

Proof of Upper Bound

X

Y

Z

a

3

m

1/5

a

2

q

1/5

b

2

q

1/5

d

3

m

1/5

a

3

q

1/5

X

Y

a

32/5

a2

1/5b

21/5

d3

1/5

Y

Z

3

m

2/5

2

q

2/5

3

q

1/5

2

m

0

Z

X

m

a

1/5

q

a

2/5

q

b

1/5

m

d

1/5

Output

Q(

D

)

R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

21

H(

XYZ

) = log |

Q

(

D

)|

Q(X,Y,Z) = R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

entropic function H

Z

X

Y

Slide22

Proof of Upper Bound

X

Y

Z

a

3

m

1/5

a

2

q

1/5

b

2

q

1/5

d

3

m

1/5

a

3

q

1/5

X

Y

a

32/5

a2

1/5b

21/5

d3

1/5

Y

Z

3

m

2/5

2

q

2/5

3

q

1/5

2

m

0

Z

X

m

a

1/5

q

a

2/5

q

b

1/5

m

d

1/5

H(

XZ

) ≤ log

N

T

H(

YZ

) ≤ log

N

S

H(

Z|Y

) ≤ log

deg

S

(

z|y

)

H(

XY

) ≤ log

N

R

Q(X,Y,Z) = R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

entropic function H

Output

Q(

D

)

R(X,Y)

S(Y,Z)

T(Z,X)

Database

D

H(

XYZ

) = log |

Q

(

D

)|

Cardinalitites

, functional dependences, max degrees

Z

X

Y

Slide23

Proof of Upper Bound

23

Q(X,Y,Z) = R(X,Y)

∧ S(Y,Z)

∧ T(Z,X)

|R|,|S|,|T| ≤

N  |Q(

D

)

|≤

N

3/2

Z

X

Y

Slide24

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(∅) = 2 h(XYZ)

= 2 log |Output|

24

Q(X,Y,Z)

=

R(X,Y)

S(Y,Z)

T(Z,X)|

R|,|S|,|T| ≤ N

 |Q(D)

|≤ N3/2

Z

X

Y

Slide25

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(∅) = 2 h(XYZ)

= 2 log |Output|

submodularity

25

Q(X,Y,Z)

=

R(X,Y)

S(Y,Z)

T(Z,X)|R|,|S|,|

T| ≤ N  |Q(

D)|≤ N

3/2

Z

X

Y

Slide26

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(

Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(∅) = 2 h(XYZ)

= 2 log |Output|

submodularity

26

Q(X,Y,Z)

=

R(X,Y)

S(Y,Z)

T(Z,X)

|R|,|S|,|T| ≤

N  |Q(

D)|≤ N

3/2

Z

X

Y

Slide27

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(

Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(∅) = 2 h(XYZ)

= 2 log |Output|

submodularity

submodularity

27

Q(X,Y,Z)

=

R(X,Y)

S(Y,Z)

T(Z,X)|

R|,|S|,|T| ≤ N

 |Q(

D)|≤ N3/2

Z

X

Y

Slide28

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(

Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(

∅) = 2 h(XYZ)

= 2 log |Output|

submodularity

submodularity

28

Q(X,Y,Z)

=

R(X,Y)

S(Y,Z)

T(Z,X)|

R|,|S|,|T| ≤

N  |Q(D

)|≤ N3/2

Z

X

Y

Slide29

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(

Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(

∅) = 2 h(

XYZ

)

= 2 log |

Q(

D

)

|

submodularity

submodularity

29

Q(X,Y,Z)

=

R(X,Y)

∧ S(Y,Z)

∧ T(Z,X)|

R|,|S|,|T| ≤ N

 |Q(D

)|≤ N3/2

Z

X

Y

Slide30

Proof of Upper Bound

3 log N

≥ h(XY) + h(YZ) + h(XZ)

≥ h(XYZ) + h(

Y) + h(XZ)

≥ h(XYZ) + h(XYZ) + h(

∅) = 2 h(

XYZ

)

= 2 log |

Q(

D

)

|

submodularity

submodularity

30

Shearer’s inequality

h(

XY

) + h(

YZ) + h(XZ) ≥ 2 h(

XYZ)

Q(X,Y,Z) = R(X,Y)

∧ S(Y,Z)

∧ T(Z,X)|R|,|

S|,|T| ≤ N  |

Q(D)|≤

N3/2

Z

X

Y

Slide31

Proof to Algorithm

h(

XY)+h(

YZ)+h(XZ)

h(XYZ)

h(Y

) + h(XZ)

h(

XYZ

)

+

31

h(

XY

) + h(

YZ

) + h(

XZ

)

≥ 2 h(

XYZ

)

Q(X,Y,Z) =

R(X,Y) ∧ S(Y,Z)

∧ T(Z,X)

Proof

Z

X

Y

Slide32

Proof to Algorithm

R(X,Y)

∧S(Y,Z)∧

T(Z,X)

h(XY)+h(

YZ)+h(XZ)

h(XYZ)

h(

Y

) + h(

XZ

)

h(

XYZ

)

+

32

h(

XY

) + h(

YZ

) + h(

XZ

) ≥ 2 h(XYZ)

Q(X,Y,Z) = R(X,Y)

∧ S(Y,Z)

∧ T(Z,X)

Proof

Algorithm

Z

X

Y

Slide33

Proof to Algorithm

R(X,Y)

∧S(Y,Z)∧

T(Z,X)

Rlight(X,Y)∧

S(Y,Z)

N

3/2

h(

XY

)+h(

YZ

)

+h(

XZ

)

h(

XYZ)

h(Y) + h(

XZ)h(

XYZ)

+

33

h(

XY

) + h(

YZ) + h(XZ) ≥ 2 h(XYZ

)

Q(X,Y,Z) = R(X,Y)

∧ S(Y,Z) ∧

T(Z,X)

Proof

Algorithm

Z

X

Y

R

light

or

R

heavy

:

degree(

Y

)

≤ or >

N

1/2

Slide34

Proof to Algorithm

R(X,Y)

∧S(Y,Z)∧

T(Z,X)

Rlight(X,Y)∧

S(Y,Z)R

heavy(Y)∧T(X,Z)

N

3/2

N

3/2

h(

XY

)+h(

YZ

)

+h(

XZ

)

h(

XYZ

)

h(Y) + h(XZ)

h(XYZ)

+

34

h(

XY

) + h(

YZ) + h(XZ

) ≥ 2 h(XYZ)

Q(X,Y,Z) =

R(X,Y) ∧

S(Y,Z) ∧

T(Z,X)

Proof

Algorithm

Z

X

Y

R

light

or

R

heavy

:

degree(

Y

)

≤ or >

N

1/2

Slide35

Proof to Algorithm

R(X,Y)

∧S(Y,Z)∧

T(Z,X)

Rlight(X,Y)∧

S(Y,Z)R

heavy(Y)∧T(X,Z)

N

3/2

N

3/2

h(

XY

)+h(

YZ

)

+h(

XZ

)

h(

XYZ

)

h(Y) + h(XZ)

h(XYZ)

+

Runtime

Õ

(

N

3/2)

35

h(XY) + h(YZ) + h(

XZ) ≥ 2 h(XYZ)

Q(X,Y,Z)

= R(X,Y) ∧

S(Y,Z) ∧

T(Z,X)

Proof

Algorithm

Z

X

Y

R

light

or

R

heavy

:

degree(

Y

)

≤ or >

N

1/2

Slide36

Enumeration Problem: Discussion

Cardinalities: [Atserias,Grohe,Marx’08, Ngo,Re,Rudra’13]Entropic bound = polymatroid bound

Algorithm for Q(D) has single log factor

Cardinalities + FDs + max degrees:Entropic bound ≨ polymatroid boundAlgorithm for

Q(D) has polylog factor

36

Slide37

Outline

Enumeration problemDecision problemConclusions

37

Slide38

Decision Problem

38

Fix Q, fix statistics on

DProblem: does Q occur in D?

Theorem

One can check if Q is in D in time

Õ(2subw(Q))

Optimal? (fine grained lower bound is open!)

“submodular width”

Slide39

Background: Tree Decomposition

Informally: TD = a tree where each node t represents an enumeration problem

Fractional hypetree width [Grohe,Marx’14]

mintree maxnode

t maxD

Submodular width [Marx’2013]maxD min

tree maxnode

t

39

Slide40

40

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

min

tree

max

node t maxD

Slide41

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u)

K(

u,x

),R(

x,y

)

41

min

tree

max

node

t

maxD

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(z,u)∧K(

u,x)

|R|,|S|,|T|,|K| ≤ N O(

N3/2) algorithm [Alon,Yuster,Zwick’97]

Slide42

R(

x,y

),S(y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(

z,u

)

K(

u,x

),R(

x,y

)

42

min

tree

max

node t maxD

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u)∧K(u,x)

|R|,|S|,|T|,|K| ≤

N O(N3/2) algorithm [Alon,Yuster,Zwick’97]

Runtime

Õ

(N2

)

(suboptimal)

Slide43

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u

)

K(

u,x

),R(

x,y

)

43

min

tree

max

node

t maxD

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u)∧K(u,x)

|R|,|S|,|T|,|K| ≤

N O(N3/2) algorithm [Alon,Yuster,Zwick’97]

Runtime

Õ

(N2

)

(suboptimal)

Slide44

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u)

K(

u,x

),R(

x,y

)

44

max

D

min

tree

maxnode t

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u)∧K(u,x)

|R|,|S|,|T|,|K|

≤ N O(N3/2) algorithm [Alon,Yuster,Zwick’97]

Slide45

R(

x,y

),S(y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(

z,u

)

K(

u,x

),R(

x,y

)

min(

max(h(

xyz

),h(zux))

, max(h(yzu),h(uxy))) =

T1

45

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u)∧K(u,x)

|R|,|S|,|T|,|K| ≤

N O(N3/2) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

Slide46

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u

)

K(

u,x

),R(

x,y

)

min(

max(h(

xyz

),h(

zux)),

max(h(yzu),h(uxy))) =

T1

T2

46

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(u,x)

|R|,|S|,|T|,|K|

≤ N O(N3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

Slide47

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u)

K(

u,x

),R(

x,y

)

min( max(h(

xyz

),h(

zux

)),

max(h(yzu),h(

uxy))) =

T1

T2

47

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u)∧K(

u,x)

|R|,|S|,|T|,|K| ≤ N O(N

3/2) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

Slide48

R(

x,y),S(

y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(z,u)

K(

u,x

),R(

x,y

)

min( max(h(xyz),h(

zux

)),

max(h(

yzu

),h(

uxy))) =

T1

T2

= max(min(h(

xyz

),h(yzu)), min(h(

xyz),h(uxy)), min(h(zux

),h(yzu)), min(h(zux),h(uxy

))) 48

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

Slide49

R(

x,y

),S(y,z)

T(

z,u),K(u,x)

Tree decompositions

S(y,z),T(

z,u

)

K(

u,x

),R(

x,y

)

= max(

min(h(

xyz

),h(yzu)),

min(h(xyz),h(uxy)),

min(h(zux),h(yzu)),

min(h(zux),h(

uxy)))

min( max(h(xyz),h(

zux)), max(h(yzu

),h(uxy))) =

T1

T2

3

log

N

≥ h(

xy) + h(yz) + h(zu

)

49

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

Slide50

S(

y,z

),T(z,u)

K(

u,x),R(x,y)

= max(min(h(xyz),h(yzu)

), min(h(xyz

),h(

uxy

)),

min(h(

zux

),h(

yzu

)),

min(h(

zux

),h(uxy)))

min( max(h(xyz),h(zux

)), max(h(yzu),h(

uxy))) =

T1

T2

3

log

N

h(xy) + h(yz) + h(

zu) ≥ h(xyz) + h(y) + h(

zu)50

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

R(

x,y

),S(

y,z

)

T(

z,u

),K(

u,x

)

Tree decompositions

Slide51

= max(

min(h(xyz),h(yzu)

), min(h(xyz),h(

uxy)),

min(h(zux),h(yzu

)), min(h(zux

),h(uxy)))

min( max(h(xyz),h(

zux

)),

max(h(

yzu

),h(

uxy

))) =

T1

T2

3

log

N

h(

xy

) + h(yz)

+ h(zu) ≥ h(xyz) + h(

y) + h(zu) ≥ h(xyz) + h(

yzu) + h(∅)

51

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

S(

y,z

),T(

z,u

)

K(

u,x

),R(

x,y

)

R(

x,y

),S(

y,z

)

T(

z,u

),K(

u,x

)

Tree decompositions

Slide52

3

log N ≥

h(xy) + h(

yz) + h(zu

) ≥ h(xyz) + h(y) + h(

zu) ≥ h(xyz) + h(yzu) + h(

∅) ≥ 2 min(h(

xyz

),h(

yzu

))

= max(

min(h(

xyz

),h(

yzu

)), min(h(xyz

),h(uxy)), min(h(

zux),h(yzu)),

min(h(zux),h(uxy

)))

min( max(h(xyz),h(zux)),

max(h(yzu),h(

uxy))) =

T1

T2

52

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

S(

y,z

),T(

z,u

)

K(

u,x

),R(

x,y

)

R(

x,y

),S(

y,z

)

T(

z,u

),K(

u,x

)

Tree decompositions

Slide53

= max(min(h(

xyz),h(yzu)), min(h(

xyz),h(uxy)), min(h(

zux),h(yzu)), min(h(zux

),h(uxy))) ≤ 3/2 log

N

min( max(h(xyz),h(zux)),

max(h(

yzu

),h(

uxy

))) =

T1

T2

subw

(

Q

) =

3/2

log

N

3

log

N

≥ h(

xy) + h(yz) + h(

zu) ≥ h(xyz) + h(y) + h(

zu) ≥ h(xyz) + h(yzu

) + h(∅) ≥ 2 min(h(xyz),h(

yzu))53

U

Z

X

Y

Q() = ∃

x∃y∃z∃u

R(

x,y

)∧S(

y,z

)∧T(

z,u

)∧K(

u,x

)

|R|,|S|,|T|,|K|

N

O(

N

3/2

) algorithm [Alon,Yuster,Zwick’97]

max

D

min

tree

max

node

t

S(

y,z

),T(

z,u

)

K(

u,x

),R(

x,y

)

R(

x,y

),S(

y,z

)

T(

z,u

),K(

u,x

)

Tree decompositions

Slide54

Proof

to Algorithm

Use the proof of: to compute the

disjunctive datalog rule:

(details omitted)

54

Runtime Õ(N

3/2

)

h(

xyz

)+h(

yzu

) ≤ h(

xy

) + h(

yz) + h(

zu)

A(x,y,z) ∨ B

(y,z,u)  R(x,y

) ∧ S(y,z) ∧ T

(z,u)

Slide55

Outline

Enumeration problemDecision problemConclusions

55

Slide56

Conclusions

Query evaluation summary:

Information theory  ProofProof

 AlgorithmOpen problems:Better “Proof

 Algorithm”Fine-grained lower bounds

56

Slide57

Thank You!

Questions?

Hung Ngo

Mahmoud Abo-Khamis

[PODS’2016]

[PODS’2017]

RelationalAI

Inc.

Slide58


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