Evaluation of Conditional Preference Queries

Evaluation of Conditional Evaluation of Conditional Preference QueriesPreference Queries

Fabíola S. Fernandes, Sandra de Amo

UNIVERSIDADE FEDERAL DE UBERLÂNDIA

PÓS-GRADUAÇÃO EMCIÊNCIA DA COMPUTAÇÃO

Page 2

MOTIVATION

UFU

Movies ( Title, Genre, Years, Director, Actor )

My preferences:

(1) I prefer those movies produced in the 90’s rather than

from 80’s, if both belongs to the same category (genre);

(2) For the movies produced in the 80’s I prefer dramas to

comedies;

(3) For Woody Allen’s films of the same genre and decade, I

prefer those staring the actress Charlotte Rampling than

those staring Mia Farrow.

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MOTIVATION

UFU

Queries:

Give the titles of the films which most fulfill my

wishes among those stored in the database,

provided they are not romance films.

Give the 4 films, among those stored in the

database, which most fulfill my wishes.

Movies ( Title, Genre, Years, Director, Actor )

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OUTLINE

UFU

The CPref-SQL Language

Related Work and Contributions

Evaluation of Preference Queries (cp-queries)

Top-K cp-queries in the RDBMS

Experimental Results

Conclusion and Further Work

Page 5

THE CPref-SQL LANGUAGE

UFU

An extension of SQL able to express conditional

preference queries

The queries incorporate the usual hard constraints

(WHERE) as well as soft constraints (preference rules)

Goals:

Express preferences over a database

Filter the answer to queries according to user

preferences

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UFU

Express preferences over a database as follows:

CREATE PREFERENCES MyPrefs FROM Movies AS

Y=90 > Y=80 [T,D,A] AND

IF D=Woody Allen THEN A=Charlotte Rampling > A=Mia Farrow

[T] AND

IF G=comedy and Y=80 THEN D=Joel Coen > D=Woody Allen

[T,A] AND

IF Y=80 THEN G=drama > G=comedy [T]

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UFU

Filter the answers to queries according to user

preferences

SELECT titleFROM movies WHERE genre <> ‘romance’ACCORDING TO PREFERENCES MyPrefs, 4

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OUTLINE

UFU







Page 9

RELATED WORK AND CONTRIBUTIONS

UFU

Topic State of the Art

Modeling and reasoning of preferences

CP-NetTCP-NetConditional Preference Formulas

SQL extensionsPreference SQLCPref-SQL

Preference OperatorsSkylineWinnowSelect-Best

Algorithms for evaluation of preference queries

BNL – skyline queriesSFSBNL+, BNL++BNL*, …

Top-K queriesTop N, Top K – rankingTop K dominating

Implementation On-top, Centrist, Built-in

Page 10

RELATED WORK AND CONTRIBUTIONS

UFU

Topic State of the Art

Modeling and reasoning of preferences

CP-NetTCP-NetConditional Preference Formulas

SQL extensionsPreference SQLCPref-SQL

Preference OperatorsSkylineWinnowSelect-Best

Algorithms for evaluation of preference queries

BNL – skyline queriesSFSBNL+, BNL++BNL*, …

Top-K queriesTop N, Top K – rankingTop K dominating

Implementation On-top, Centrist, Built-in

Proposal

Conditional Preference Formulas

CPref-SQL

SelectK-Best

BNL** e R-BNL**

Top-K cp-queries

Built-in

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CONTRIBUTIONS

UFU

Top-K cp-queries

Algorithms BNL** and R-BNL** for evaluating the

Select-Best and SelectK-Best operators

Implementation in the core of the RDBMS PostgreSQL

Experiments comparing the built-in approach with the

translation into standard SQL

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OUTLINE

UFU



Evaluation of Preference Queries (cp-

queries)




Page 13

THE PREFERENCE MODEL

UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor

t1 Titanic drama 90James

CameronBill Paxton

t2Stardust

Memoriesdrama 80 Woody Allen

Charlotte Rampling

t3 New York Stories drama 80 Woody Allen Mia Farrow

t4A Midsummer

Night's Sex Comedy

comedy

80 Woody Allen Mia Farrow

t5 Crimewavecomed

y80 Joel Coen Louise Lasser

t6 Avatar action 00James

CameronZoe Saldana

t1

t2

t3

t4

t5

t6

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UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor


CameronBill Paxton

t2Stardust Memories

drama 80 Woody AllenCharlotte Rampling

t3New York Stories

drama 80 Woody Allen Mia Farrow

t4A Midsummer

Night's Sex Comedy

comedy


t5 Crimewavecomed



CameronZoe Saldana

t1

t2

t3

t4

t5

t6

R2

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UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor


CameronBill Paxton

t2Stardust


Charlotte Rampling

t3New York Stories

drama 80 Woody Allen Mia Farrow

t4A Midsummer Night's Sex

Comedy

comedy


t5 Crimewave comedy 80 Joel Coen Louise Lasser


CameronZoe Saldana

t1

t2

t3

t4

t5

t6

R2

R4

Page 16


UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor


CameronBill Paxton

t2Stardust


Charlotte Rampling


t4A Midsummer

Night's Sex Comedy

comedy


t5 Crimewavecomed



CameronZoe Saldana

t1

t2

t3

t4

t5

t6

R2

R4

Page 17


UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor


CameronBill Paxton

t2Stardust


Charlotte Rampling


t4A Midsummer

Night's Sex Comedy

comedy


t5 Crimewavecomed



CameronZoe Saldana

t1

t2

t3

t4

t5

t6

R2

R4

Page 18


UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}].

Title GenreYears

Director Actor


CameronBill Paxton

t2Stardust


Charlotte Rampling


t4A Midsummer

Night's Sex Comedy

comedy


t5 Crimewavecomed



CameronZoe Saldana

t1

t2

t3

t4

t5

t6

R2

R4

Page 19

CONSISTENCY TEST [Wilson 2004]

UFU

R1: (Y = 90) > (Y = 80) [{T,D,A}],

R2: (D = wa) (A = cr) > (A = mf) [{T}],

R3: (G = c) ^ (Y = 80) (D = jc) > (D = wa)

[{T,A}],

R4: (Y = 80) (G = d) > (G = c) [{T}],

R5: (G = c) (Y=80) > (Y = 90) [{T}]

When a cp-theory is consistent?

Dependency Graph Local Consistency

Y

GD

A

T

Years

90 80

(x, c, 80, y, z) (x, c, 90, y, z) ?

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CPref-SQL ALGEBRA OPERATORS

R

Tuples that do not have any other tuple over them in the preference hierarchy

K

R

K tuples with the less number of tuples above them in the preference

hierarchy

Select-Best

SelectK-Best

UFU

Page 21 UFU

π

SelectBest / SelectK-Best

|X|

R1 Rn...

SELECT < attribute-list >

FROM < tables >

WHERE < where-conditions (hard

conditions) >

ACCORDING TO PREFERENCES

< preference (soft conditions) >

CPref-SQL ALGEBRA OPERATORS

Page 22

ALGORITHMS BNL** E R-BNL**

UFU

SelectBest

BNL**

SelectK-Best

R-BNL**

Follows the lines of the Blocked Nested Loop (BNL)

algorithm (BORZSONYI et al. ICDE 2001)

Uses the structure of a Datalog program to compare

tuples

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UFU

Dominance Test

Input: tuples t1, t2

Output: t1 > t2 or t2 > t1 or t1 ~ t2 (incomparable)

Method

1. Projection

2. CP-Theory = Datalog program

Test t1 > t2 : datalog goal

Page 24 UFU

Dominance Test: t1 > t2? t2 > t1? t1 ~ t2?

CP-theory

1. A = a1 -> C = c1 > C

= c2

2. B = b1 -> C = c2 > C

= c3

R (A, B, C, D)

dom(A) = {a1, a2, a3}dom(B) = {b1, b2}dom(C) = {c1, c2, c3}dom(D) = {d1, d2}

t1, t2 Are comparable?

(a1,b1,c1,d1) and (a1,b1,c1,d2) No

(a2,b1,c1,d1) and (a1,b1,c3,d1) No

(a2,b2,c2,d1) and (a2,b2,c3,d1) Yes

(a1,b1,c2,d1) and (a1,b1,c3,d1) Yes

(a2,b2,c2) and (a2,b2,c3)

(a1,b1,c2) and (a1,b1,c3)

Projections

1. Projection


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UFU


2. CP-theory = Datalog Program

Converts the preference rules in a Datalog program P

Each dominance test of 2 comparable tuples is a goal for P

Finds the solution (goal) using SLD resolution method

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UFU


CP-Theory

1. A = a1 -> C = c1 > C

= c2

2. B = b1 -> C = c2 > C

= c3Datalog Program

1. pref(x1, y1, z1, x2, y2, z2 ) <- x1 = a1, x2 = a1, y1 = y2, z1 = c1 ,

z2 = c2

2. pref(x1, y1, z1, x2, y2, z2 ) <- x1 = x2, y1 = b1, y2, = b1, z1 = c2 ,

z2 = c3

3. dom( x1, y1, z1, x2, y2, z2 ) <- pref( x1, y1, z1, x2, y2, z2 )

4. dom( x1, y1, z1, x2, y2, z2 ) <- pref( x1, y1, z1, x3, y3, z3 ),

dom( x3, y3, z3, x2, y2, z2 )

Page 27


UFU


2. CP-theory = Datalog Program

Test

(a1, b1, c1) > (a1, b1, c2)

Goal

dom( a1, b1, c1, a1, b1, c2 )

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ALGORITHM BNL**

UFU

Procedure MostPref(r)

1. clear the in-memory page W and the temporary table F

2. make r the input

3. repeat the following until the input is empty

4. forfor every tuple t in the input

5. ifif t is dominated by a tuple in W

6. thenthen ignore t

7. ifif t dominates some tuples in W

8. thenthen eliminate the dominated tuples and insert t into W

9. ifif t is incomparable with all tuples in W

10. thenthen insert t into W if there is room, otherwise add t to F

11. insert in S the tuples of W which were added there when F was empty

12. make F the input, clear the temporary table

13. return S

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ALGORITHM R-BNL**

UFU

t1

t2

t3

t4

t5

t6

Output S

t1[]

t2[t1]

t3[t1,t2]

t4[t1,t2,t3,t5]t5[]

t6[]

l(t) = 0 t’ r| t’ > t max { l(t’) | t’ > t } + 1 otherwise

0 0

01

2

3

SelectK-Best ( 4, R ) = { t1, t5, t6, t2}

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ALGORITHM R-BNL**

UFU

Procedure topK(r)

1. clear the in-memory page W and the temporary table F

2. make r the input

3. repeat the following until the input is empty

4.4. forfor every tuple t in the input

5. for every tuple t’ in W

6.6. ifif t is dominated by t’

7.7. thenthen add t’ into MorePref(t)

8.8. ifif t dominates t’

9.9. thenthen add t into MorePref(t’)

10. insert t into W if there is room, otherwise add t to F

11. insert in S the tuples of W which were added there when F was

empty

12. make F the input, clear the temporary table

13. Return S

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OUTLINE

UFU







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TOP-K CP-QUERIES IN THE RDBMS

UFU

Extension for PostgreSQL 8.4

Linux Operational System

C Language

Directly implemented in the Postgres back-end: built-in

approach

Page 33 UFU

Postgresback-end

CREATE PREFERENCES


Page 34 UFU

Postgresback-end

ACCORDING TO

PREFERENCES


Page 35 UFU


Page 36

OUTLINE

UFU







Page 37

EXPERIMENTAL RESULTS

UFU

Benchmark TPC-H (http://www.tpc.org/tpch/)

Synthetic database

Suite with 22 SQL queries

Queries adaptation:

insertion of the preference clause

removal of aggregate functions (group by, having, …)

changes on the terms of the WHERE clause

Performance and scalability evaluations of CPref-

SQL queries and their translations to SQL

All CPref-SQL query can be translated into SQL queries

with recursion

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UFU

Conversion CPref-SQL <-> SQL

CREATE OR REPLACE VIEW Rules (title,genre,years,director,actor,tit,gen,yea,dir,act) AS

(SELECT * FROM movies M, movies M1 WHERE M.genre = ‘drama' AND M1.genre = ‘musical'

AND M.director = M1.director AND M.years = M1.years)

UNION (SELECT * FROM movies M, movies M1 WHERE M.years = 90 AND M1.years = 80 and M.genre

= M1.genre) UNION (SELECT * FROM movies M, movies M1 WHERE M.years = 80 and M1.years = 80 and M.genre =

‘drama’ and M1.genre = ‘comedy’ and M.director = M1.director and M.actor = M1.actor);

CREATE PREFERENCES mypref

FROM movies AS

genre = ‘drama > genre = ‘musical’ [1,5]

AND

years = 90 > years = 80 [1,4,5]

AND

IF years = 80 THEN genre = ‘drama’ > genre = ‘comedy’ [1]

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UFU

Conversion CPref-SQL <-> SQL WITH RECURSIVE Recursion ( tit, gen, yea, dir, act, title, genre, years, director, actor ) AS (

( SELECT * FROM Rules )UNION

( SELECT M.title, M.genre, M.years, M.director, M.actor, R.title, R.genre, R.years, R.director, R.actor

FROM Rules M, Recursion R WHERE M.tit = R.tit AND

M.gen = R.gen AND M.yea = R.yea M.dir = R.dir AND M.act = R.act ) )

SELECT *

FROM movies

WHERE genre <> ‘romance’

ACCORDING TO PREFERENCES

mypref

SELECT *

FROM movies

WHERE genre <> ‘romance’

EXCEPT

SELECT R.title, R.genre, R.years, R.director, R.actor

FROM Recursion R;

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UFU

Performance

Page 41


UFU

Scalability

Page 42

OUTLINE

UFU







Page 43

CONCLUSION AND FURTHER WORK

UFU

Development of algorithms under the approach on-top

Supporting to other built-in predicates (>, <, >=,…)

Ongoing research:

Top-K cp-queries

Algorithms BNL** e R-BNL**

Implementation in the core of the PostgreSQL

Incorporating aggregate operations in the CPref-SQL

block

Optimization of the execution plan – rules rewrite

Future research:

Evaluation of Conditional Evaluation of Conditional Preference QueriesPreference Queries

UNIVERSIDADE FEDERAL DE UBERLÂNDIA

PÓS-GRADUAÇÃO EMCIÊNCIA DA COMPUTAÇÃO

!! FIM !!

[email protected], [email protected]

Fabíola S. Fernandes, Sandra de Amo

Page 45

ALGORITHM BNL**

UFU

Block of Pages

Buffer

Output page

Input Table

Temporary table F

The Preferred tuples = query

answer

Window W

Page 46

ALGORITHM BNL**

UFU

t1

t2

t3

t4

t5

t6

INPUT Output S

Buffer W

t1

t2

t3

t4

t5

t6

Temporary table F

t1

t5

t6

t6

Page 47

ALGORITHM R-BNL**

UFU

Block of Pages

Buffer

Output page

Input Table

Temporary table F

Window W

All tuples with their respective MorePref lists

Page 48

ALGORITHM R-BNL**

UFU

t1

t2

t3

t4

t5

t6

INPUT Output S

Buffer W

t1[]

t2[]

t3[]

t4[]

t5[]

t6[]

Temporary table F

t2[t1]

t3[t1,t2]

t4[t1,t2]

Page 49

ALGORITHM R-BNL**

UFU

t1

t2

t3

t4

t5

t6

INPUT Output S

Buffer W

t1[]

Temporary table F

t2[t1]

t3[t1,t2]

t5[]

t6[]

t4[t1,t2]

Page 50

ALGORITHM R-BNL**

UFU

t1

t2

t3

t4

t5

t6

Output S

t1[]

t2[t1]

t3[t1,t2]

t4[t1,t2,t3,t5]t5[]

t6[]

l(t) = 0 t’ r| t’ > t max { l(t’) | t’ > t } + 1 otherwise

0 0

01

2

3

SelectK-Best ( 4, R ) = { t1, t5, t6, t2}

Page 51 UFU

Create Preferences

Catalog structure:

Each column is an attribute of the relation

movies

Each row represents a ruleID

Ruletitle

genreyear

sdirector actor

1 NIL * 90|80 NIL NIL

2 NIL * * Woody Allen Charlotte Rampling|Mia Ferrow

3 NIL comedy 80 Joel Coen|Woody Allen NIL

4 NIL drama|comedy 80 * *


Page 52 UFU

Create Preferences


Page 53 UFU

Insertion of the operators in the query processor


Documents

Evaluation of Conditional Preference Queries