Preferences, Queries, Logics
Transcript of Preferences, Queries, Logics
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Preferences, Queries, Logics
Jan ChomickiUniversity at Buffalo
DBRank, August 29, 2011
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 1 / 46
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Plan of the talk
1 Preference relations
2 Preference queries
3 Advanced topics
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Part I
Preference relations
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Motivation
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Preference relations
Universe of objects
constants: uninterpreted, numbers,...
individuals (entities)
tuples
sets
Preference relation ¡
binary relation between objects
x ¡ y � x is better than y � x dominates y
an abstract, uniform way of talking about (relative) desirability,worth, cost, timeliness,..., and their combinations
preference relations used in preference queries
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Preference relations
Universe of objects
constants: uninterpreted, numbers,...
individuals (entities)
tuples
sets
Preference relation ¡
binary relation between objects
x ¡ y � x is better than y � x dominates y
an abstract, uniform way of talking about (relative) desirability,worth, cost, timeliness,..., and their combinations
preference relations used in preference queries
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
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Buying a car
Salesman: What kind of car do you prefer?
Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.
Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?
Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 6 / 46
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.
Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 6 / 46
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.
Customer: Wait...it better be a BMW.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 6 / 46
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Buying a car
Salesman: What kind of car do you prefer?Customer: The newer the better, if it is the same make. And cheap, too.Salesman: Which is more important for you: the age or the price?Customer: The age, definitely.Salesman: Those are the best cars, according to your preferences, that wehave in stock.Customer: Wait...it better be a BMW.
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Preferences in perspective
Applications of preferences and preference queries
1 decision making
2 e-commerce
3 digital libraries
4 personalization
Preferences are multi-disciplinary
economic theory: von Neumann, Arrow, Sen
philosophy: Aristotle, von Wright
psychology: Slovic
artificial intelligence: Boutilier, Brafman
databases: Kießling, Kossmann
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Preferences in perspective
Applications of preferences and preference queries
1 decision making
2 e-commerce
3 digital libraries
4 personalization
Preferences are multi-disciplinary
economic theory: von Neumann, Arrow, Sen
philosophy: Aristotle, von Wright
psychology: Slovic
artificial intelligence: Boutilier, Brafman
databases: Kießling, Kossmann
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Preferences in perspective
Applications of preferences and preference queries
1 decision making
2 e-commerce
3 digital libraries
4 personalization
Preferences are multi-disciplinary
economic theory: von Neumann, Arrow, Sen
philosophy: Aristotle, von Wright
psychology: Slovic
artificial intelligence: Boutilier, Brafman
databases: Kießling, Kossmann
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 7 / 46
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Properties of preference relations
Properties of ¡
irreflexivity: @x . x £ x
asymmetry: @x , y . x ¡ y ñ y £ x
transitivity: @x , y , z . px ¡ y ^ y ¡ zq ñ x ¡ z
negative transitivity: @x , y , z . px £ y ^ y £ zq ñ x £ z
connectivity: @x , y . x ¡ y _ y ¡ x _ x � y
Orders
strict partial order (SPO): irreflexive and transitive
weak order (WO): negatively transitive SPO
total order: connected SPO
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Properties of preference relations
Properties of ¡
irreflexivity: @x . x £ x
asymmetry: @x , y . x ¡ y ñ y £ x
transitivity: @x , y , z . px ¡ y ^ y ¡ zq ñ x ¡ z
negative transitivity: @x , y , z . px £ y ^ y £ zq ñ x £ z
connectivity: @x , y . x ¡ y _ y ¡ x _ x � y
Orders
strict partial order (SPO): irreflexive and transitive
weak order (WO): negatively transitive SPO
total order: connected SPO
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Properties of preference relations
Properties of ¡
irreflexivity: @x . x £ x
asymmetry: @x , y . x ¡ y ñ y £ x
transitivity: @x , y , z . px ¡ y ^ y ¡ zq ñ x ¡ z
negative transitivity: @x , y , z . px £ y ^ y £ zq ñ x £ z
connectivity: @x , y . x ¡ y _ y ¡ x _ x � y
Orders
strict partial order (SPO): irreflexive and transitive
weak order (WO): negatively transitive SPO
total order: connected SPO
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Weak and total orders
Weak order
a b
c d e
f
Total order
a
d
f
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Order properties of preference relations
Irreflexivity, asymmetry: uncontroversial.
Transitivity:
captures rationality of preference
not always guaranteed: voting paradoxes
helps with preference querying
Negative transitivity:
scoring functions represent weak orders
We assume that preference relations are SPOs.
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Order properties of preference relations
Irreflexivity, asymmetry: uncontroversial.
Transitivity:
captures rationality of preference
not always guaranteed: voting paradoxes
helps with preference querying
Negative transitivity:
scoring functions represent weak orders
We assume that preference relations are SPOs.
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Order properties of preference relations
Irreflexivity, asymmetry: uncontroversial.
Transitivity:
captures rationality of preference
not always guaranteed: voting paradoxes
helps with preference querying
Negative transitivity:
scoring functions represent weak orders
We assume that preference relations are SPOs.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 10 / 46
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Order properties of preference relations
Irreflexivity, asymmetry: uncontroversial.
Transitivity:
captures rationality of preference
not always guaranteed: voting paradoxes
helps with preference querying
Negative transitivity:
scoring functions represent weak orders
We assume that preference relations are SPOs.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 10 / 46
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Order properties of preference relations
Irreflexivity, asymmetry: uncontroversial.
Transitivity:
captures rationality of preference
not always guaranteed: voting paradoxes
helps with preference querying
Negative transitivity:
scoring functions represent weak orders
We assume that preference relations are SPOs.
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Not every SPO is a WO
Indifference �
x � y � x £ y ^ y £ x .
Canonical example
mazda ¡ kia, mazda �i vw, kia �i vw
Violation of negative transitivity
mazda £ vw, vw £ kia, mazda ¡ kia
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Not every SPO is a WO
Indifference �
x � y � x £ y ^ y £ x .
Canonical example
mazda ¡ kia, mazda �i vw, kia �i vw
Violation of negative transitivity
mazda £ vw, vw £ kia, mazda ¡ kia
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Not every SPO is a WO
Indifference �
x � y � x £ y ^ y £ x .
Canonical example
mazda ¡ kia, mazda �i vw, kia �i vw
Violation of negative transitivity
mazda £ vw, vw £ kia, mazda ¡ kia
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Not every SPO is a WO
Indifference �
x � y � x £ y ^ y £ x .
Canonical example
mazda ¡ kia, mazda �i vw, kia �i vw
Violation of negative transitivity
mazda £ vw, vw £ kia, mazda ¡ kia
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 12 / 46
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 12 / 46
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions:
F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 12 / 46
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � p
pm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 12 / 46
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Preference specification
Explicit preference relations
Finite sets of pairs: bmw ¡ mazda, mazda ¡ kia,...
Implicit preference relations
can be infinite but finitely representable
defined using logic formulas in some constraint theory:
pm1, y1, p1q ¡1 pm2, y2, p2q � y1 ¡ y2 _ py1 � y2 ^ p1 p2q
for relation CarpMake,Year ,Priceq.
defined using preference constructors (Preference SQL)
defined using real-valued scoring functions: F pm, y , pq � α � y � β � ppm1, y1, p1q ¡2 pm2, y2, p2q � F pm1, y1, p1q ¡ F pm2, y2, p2q
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Logic formulas
The language of logic formulasconstants
object (tuple) attributes
comparison operators: �, ��, ,¡, . . .
arithmetic operators: �, �, . . .
Boolean connectives: ,^,_
quantifiers:� @, D� usually can be eliminated (quantifier elimination)
no database relations
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Logic formulas
The language of logic formulasconstants
object (tuple) attributes
comparison operators: �, ��, ,¡, . . .
arithmetic operators: �, �, . . .
Boolean connectives: ,^,_
quantifiers:� @, D� usually can be eliminated (quantifier elimination)
no database relations
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Representability
Definition
A scoring function f represents a preference relation ¡ if for all x , y
x ¡ y � f pxq ¡ f pyq.
Necessary condition for representability
The preference relation ¡ is a weak order.
Sufficient condition for representability
¡ is a weak order
the domain is countable or some continuity conditions are satisfied(studied in decision theory)
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Representability
Definition
A scoring function f represents a preference relation ¡ if for all x , y
x ¡ y � f pxq ¡ f pyq.
Necessary condition for representability
The preference relation ¡ is a weak order.
Sufficient condition for representability
¡ is a weak order
the domain is countable or some continuity conditions are satisfied(studied in decision theory)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 14 / 46
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Representability
Definition
A scoring function f represents a preference relation ¡ if for all x , y
x ¡ y � f pxq ¡ f pyq.
Necessary condition for representability
The preference relation ¡ is a weak order.
Sufficient condition for representability
¡ is a weak order
the domain is countable or some continuity conditions are satisfied(studied in decision theory)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 14 / 46
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Representability
Definition
A scoring function f represents a preference relation ¡ if for all x , y
x ¡ y � f pxq ¡ f pyq.
Necessary condition for representability
The preference relation ¡ is a weak order.
Sufficient condition for representability
¡ is a weak order
the domain is countable or some continuity conditions are satisfied(studied in decision theory)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 14 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.
POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.
NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.
HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Preference constructors [Kießling, 2002]
Good values
Prefer v P S1 over v R S1.POS(Make,tmazda,vwu)
Bad values
Prefer v R S1 over v P S1.NEG(Make,tyugou)
Explicit preference
Preference encoded by a finitedirected graph.
EXP(Make,t(bmw,ford),...,(mazda,kia)u)
Value comparison
Prefer larger/smaller values.HIGHEST(Year)
LOWEST(Price)
Distance
Prefer values closer to v0.
AROUND(Price,12K)
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 16 / 46
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Combining preferences
Preference composition
combining preferences about objects of the same kind
dimensionality is not increased
representing preference aggregation, revision, ...
Preference accumulation
defining preferences over objects in terms of preferences over simplerobjects
dimensionality is increased (preferences over Cartesian product).
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 17 / 46
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Combining preferences
Preference composition
combining preferences about objects of the same kind
dimensionality is not increased
representing preference aggregation, revision, ...
Preference accumulation
defining preferences over objects in terms of preferences over simplerobjects
dimensionality is increased (preferences over Cartesian product).
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 17 / 46
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Combining preferences
Preference composition
combining preferences about objects of the same kind
dimensionality is not increased
representing preference aggregation, revision, ...
Preference accumulation
defining preferences over objects in terms of preferences over simplerobjects
dimensionality is increased (preferences over Cartesian product).
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 17 / 46
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Combining preferences: composition
Boolean composition
x ¡Y y � x ¡1 y _ x ¡2 y
and similarly for X.
Prioritized composition
x ¡lex y � x ¡1 y _ py £1 x ^ x ¡2 yq.
Pareto composition
x ¡Par y � px ¡1 y ^ y £2 xq _ px ¡2 y ^ y £1 xq.
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Combining preferences: composition
Boolean composition
x ¡Y y � x ¡1 y _ x ¡2 y
and similarly for X.
Prioritized composition
x ¡lex y � x ¡1 y _ py £1 x ^ x ¡2 yq.
Pareto composition
x ¡Par y � px ¡1 y ^ y £2 xq _ px ¡2 y ^ y £1 xq.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 18 / 46
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Combining preferences: composition
Boolean composition
x ¡Y y � x ¡1 y _ x ¡2 y
and similarly for X.
Prioritized composition
x ¡lex y � x ¡1 y _ py £1 x ^ x ¡2 yq.
Pareto composition
x ¡Par y � px ¡1 y ^ y £2 xq _ px ¡2 y ^ y £1 xq.
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 18 / 46
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Combining preferences: composition
Boolean composition
x ¡Y y � x ¡1 y _ x ¡2 y
and similarly for X.
Prioritized composition
x ¡lex y � x ¡1 y _ py £1 x ^ x ¡2 yq.
Pareto composition
x ¡Par y � px ¡1 y ^ y £2 xq _ px ¡2 y ^ y £1 xq.
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Preference composition
Preference relation ¡1
bmw
ford mazda
kia
Preference relation ¡2
bmw
ford
mazda
kia
Prioritized composition
bmw
ford
mazda
kia
Pareto composition
bmwford
mazdakia
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Preference composition
Preference relation ¡1
bmw
ford mazda
kia
Preference relation ¡2
bmw
ford
mazda
kia
Prioritized composition
bmw
ford
mazda
kia
Pareto composition
bmwford
mazdakia
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 19 / 46
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Preference composition
Preference relation ¡1
bmw
ford mazda
kia
Preference relation ¡2
bmw
ford
mazda
kia
Prioritized composition
bmw
ford
mazda
kia
Pareto composition
bmwford
mazdakia
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 19 / 46
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Preference composition
Preference relation ¡1
bmw
ford mazda
kia
Preference relation ¡2
bmw
ford
mazda
kia
Prioritized composition
bmw
ford
mazda
kia
Pareto composition
bmwford
mazdakia
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 19 / 46
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Preference composition
Preference relation ¡1
bmw
ford mazda
kia
Preference relation ¡2
bmw
ford
mazda
kia
Prioritized composition
bmw
ford
mazda
kia
Pareto composition
bmwford
mazdakia
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 19 / 46
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Combining preferences: accumulation [Kiessling, 2002]
Prioritized accumulation: ¡pr� p¡1 & ¡2q
px1, x2q ¡pr py1, y2q � x1 ¡1 y1 _ px1 � y1 ^ x2 ¡2 y2q.
Pareto accumulation: ¡pa� p¡1 b ¡2q
px1, x2q ¡pa py1, y2q � px1 ¡1 y1 ^ x2 ©2 y2q _ px1 ©1 y1 ^ x2 ¡2 y2q.
Properties
closure
associativity
commutativity of Pareto accumulation
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Combining preferences: accumulation [Kiessling, 2002]
Prioritized accumulation: ¡pr� p¡1 & ¡2q
px1, x2q ¡pr py1, y2q � x1 ¡1 y1 _ px1 � y1 ^ x2 ¡2 y2q.
Pareto accumulation: ¡pa� p¡1 b ¡2q
px1, x2q ¡pa py1, y2q � px1 ¡1 y1 ^ x2 ©2 y2q _ px1 ©1 y1 ^ x2 ¡2 y2q.
Properties
closure
associativity
commutativity of Pareto accumulation
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 20 / 46
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Combining preferences: accumulation [Kiessling, 2002]
Prioritized accumulation: ¡pr� p¡1 & ¡2q
px1, x2q ¡pr py1, y2q � x1 ¡1 y1 _ px1 � y1 ^ x2 ¡2 y2q.
Pareto accumulation: ¡pa� p¡1 b ¡2q
px1, x2q ¡pa py1, y2q � px1 ¡1 y1 ^ x2 ©2 y2q _ px1 ©1 y1 ^ x2 ¡2 y2q.
Properties
closure
associativity
commutativity of Pareto accumulation
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 20 / 46
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Combining preferences: accumulation [Kiessling, 2002]
Prioritized accumulation: ¡pr� p¡1 & ¡2q
px1, x2q ¡pr py1, y2q � x1 ¡1 y1 _ px1 � y1 ^ x2 ¡2 y2q.
Pareto accumulation: ¡pa� p¡1 b ¡2q
px1, x2q ¡pa py1, y2q � px1 ¡1 y1 ^ x2 ©2 y2q _ px1 ©1 y1 ^ x2 ¡2 y2q.
Properties
closure
associativity
commutativity of Pareto accumulation
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Skylines
Skyline
Given single-attribute total preference relations ¡A1 , . . . ,¡An for arelational schema RpA1, . . . ,Anq, the skyline preference relation ¡sky isdefined as
¡sky�¡A1 b ¡A2 b � � �b ¡An .
Unfolding the definition
px1, . . . , xnq ¡sky py1, . . . , ynq �
©
i
xi ©Aiyi ^ª
i
xi ¡Aiyi .
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Skylines
Skyline
Given single-attribute total preference relations ¡A1 , . . . ,¡An for arelational schema RpA1, . . . ,Anq, the skyline preference relation ¡sky isdefined as
¡sky�¡A1 b ¡A2 b � � �b ¡An .
Unfolding the definition
px1, . . . , xnq ¡sky py1, . . . , ynq �
©
i
xi ©Aiyi ^ª
i
xi ¡Aiyi .
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Skyline in Euclidean space
Two-dimensional Euclidean space
px1, x2q ¡sky py1, y2q � x1 ¥ y1 ^ x2 ¡ y2 _ x1 ¡ y1 ^ x2 ¥ y2
Skyline consists of ¡sky -maximal vectors
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Skyline in Euclidean space
Two-dimensional Euclidean space
px1, x2q ¡sky py1, y2q � x1 ¥ y1 ^ x2 ¡ y2 _ x1 ¡ y1 ^ x2 ¥ y2
Skyline consists of ¡sky -maximal vectors
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Skyline in Euclidean space
Two-dimensional Euclidean space
px1, x2q ¡sky py1, y2q � x1 ¥ y1 ^ x2 ¡ y2 _ x1 ¡ y1 ^ x2 ¥ y2
Skyline consists of ¡sky -maximal vectors
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Skyline properties
Invariance
A skyline preference relation is unaffacted by scaling or shifting in anydimension.
Maxima
A skyline consists of the maxima of monotonic scoring functions.
Skyline is not a weak order
p2, 0q £sky p0, 2q, p0, 2q £sky p1, 0q, p2, 0q ¡sky p1, 0q
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Skyline properties
Invariance
A skyline preference relation is unaffacted by scaling or shifting in anydimension.
Maxima
A skyline consists of the maxima of monotonic scoring functions.
Skyline is not a weak order
p2, 0q £sky p0, 2q, p0, 2q £sky p1, 0q, p2, 0q ¡sky p1, 0q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 23 / 46
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Skyline properties
Invariance
A skyline preference relation is unaffacted by scaling or shifting in anydimension.
Maxima
A skyline consists of the maxima of monotonic scoring functions.
Skyline is not a weak order
p2, 0q £sky p0, 2q, p0, 2q £sky p1, 0q, p2, 0q ¡sky p1, 0q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 23 / 46
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Skyline properties
Invariance
A skyline preference relation is unaffacted by scaling or shifting in anydimension.
Maxima
A skyline consists of the maxima of monotonic scoring functions.
Skyline is not a weak order
p2, 0q £sky p0, 2q, p0, 2q £sky p1, 0q, p2, 0q ¡sky p1, 0q
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 23 / 46
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Skyline in SQL
Grouping
Designating attributes not used in comparisons (DIFF).
Example
SELECT * FROM Car
SKYLINE Price MIN,
Year MAX,
Make DIFF
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Skyline in SQL
Grouping
Designating attributes not used in comparisons (DIFF).
Example
SELECT * FROM Car
SKYLINE Price MIN,
Year MAX,
Make DIFF
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Part II
Preference queries
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Winnow [Ch., 2002]
Winnow
new relational algebra operator ω (other names: Best, BMO)
retrieves the non-dominated (best) elements in a database relation
can be expressed in terms of other operators
Definition
Given a preference relation ¡ and a database relation r :
ω¡prq � tt P r | Dt 1 P r . t 1 ¡ tu.
Notation: If a preference relation ¡C is defined using a formula C , thenwe write ωC prq, instead of ω¡C
prq.
Skyline query
ω¡
sky prq computes the set of maximal vectors in r (the skyline set).
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Winnow [Ch., 2002]
Winnow
new relational algebra operator ω (other names: Best, BMO)
retrieves the non-dominated (best) elements in a database relation
can be expressed in terms of other operators
Definition
Given a preference relation ¡ and a database relation r :
ω¡prq � tt P r | Dt 1 P r . t 1 ¡ tu.
Notation: If a preference relation ¡C is defined using a formula C , thenwe write ωC prq, instead of ω¡C
prq.
Skyline query
ω¡
sky prq computes the set of maximal vectors in r (the skyline set).
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Winnow [Ch., 2002]
Winnow
new relational algebra operator ω (other names: Best, BMO)
retrieves the non-dominated (best) elements in a database relation
can be expressed in terms of other operators
Definition
Given a preference relation ¡ and a database relation r :
ω¡prq � tt P r | Dt 1 P r . t 1 ¡ tu.
Notation: If a preference relation ¡C is defined using a formula C , thenwe write ωC prq, instead of ω¡C
prq.
Skyline query
ω¡
sky prq computes the set of maximal vectors in r (the skyline set).
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Winnow [Ch., 2002]
Winnow
new relational algebra operator ω (other names: Best, BMO)
retrieves the non-dominated (best) elements in a database relation
can be expressed in terms of other operators
Definition
Given a preference relation ¡ and a database relation r :
ω¡prq � tt P r | Dt 1 P r . t 1 ¡ tu.
Notation: If a preference relation ¡C is defined using a formula C , thenwe write ωC prq, instead of ω¡C
prq.
Skyline query
ω¡
sky prq computes the set of maximal vectors in r (the skyline set).
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Winnow [Ch., 2002]
Winnow
new relational algebra operator ω (other names: Best, BMO)
retrieves the non-dominated (best) elements in a database relation
can be expressed in terms of other operators
Definition
Given a preference relation ¡ and a database relation r :
ω¡prq � tt P r | Dt 1 P r . t 1 ¡ tu.
Notation: If a preference relation ¡C is defined using a formula C , thenwe write ωC prq, instead of ω¡C
prq.
Skyline query
ω¡
sky prq computes the set of maximal vectors in r (the skyline set).
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Example of winnow
Relation CarpMake,Year ,Priceq
Preference relation:
pm, y , pq ¡1 pm1, y 1, p1q � y ¡ y 1 _ py � y 1 ^ p p1q.
Make Year Price
mazda 2009 20K
ford 2009 15K
ford 2007 12K
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Example of winnow
Relation CarpMake,Year ,Priceq
Preference relation:
pm, y , pq ¡1 pm1, y 1, p1q � y ¡ y 1 _ py � y 1 ^ p p1q.
Make Year Price
mazda 2009 20K
ford 2009 15K
ford 2007 12K
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Example of winnow
Relation CarpMake,Year ,Priceq
Preference relation:
pm, y , pq ¡1 pm1, y 1, p1q � y ¡ y 1 _ py � y 1 ^ p p1q.
Make Year Price
mazda 2009 20K
ford 2009 15K
ford 2007 12K
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Example of winnow
Relation CarpMake,Year ,Priceq
Preference relation:
pm, y , pq ¡1 pm1, y 1, p1q � y ¡ y 1 _ py � y 1 ^ p p1q.
Make Year Price
mazda 2009 20K
ford 2009 15K
ford 2007 12K
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Generalizations of winnow
Iterating winnow
ω0¡prq � H
ωn�1¡prq � ω¡pr �
�0¤i¤n ω
i¡prqq
Ranking
Rank tuples by their minimum distance from a winnow tuple:
η¡prq � tpt, iq | t P ωiC prqu.
k-band
Return the tuples dominated by at most k tuples:
ω¡prq � tt P r | #tt 1 P r | t 1 ¡ tu ¤ ku.
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Generalizations of winnow
Iterating winnow
ω0¡prq � H
ωn�1¡prq � ω¡pr �
�0¤i¤n ω
i¡prqq
Ranking
Rank tuples by their minimum distance from a winnow tuple:
η¡prq � tpt, iq | t P ωiC prqu.
k-band
Return the tuples dominated by at most k tuples:
ω¡prq � tt P r | #tt 1 P r | t 1 ¡ tu ¤ ku.
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Generalizations of winnow
Iterating winnow
ω0¡prq � H
ωn�1¡prq � ω¡pr �
�0¤i¤n ω
i¡prqq
Ranking
Rank tuples by their minimum distance from a winnow tuple:
η¡prq � tpt, iq | t P ωiC prqu.
k-band
Return the tuples dominated by at most k tuples:
ω¡prq � tt P r | #tt 1 P r | t 1 ¡ tu ¤ ku.
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Generalizations of winnow
Iterating winnow
ω0¡prq � H
ωn�1¡prq � ω¡pr �
�0¤i¤n ω
i¡prqq
Ranking
Rank tuples by their minimum distance from a winnow tuple:
η¡prq � tpt, iq | t P ωiC prqu.
k-band
Return the tuples dominated by at most k tuples:
ω¡prq � tt P r | #tt 1 P r | t 1 ¡ tu ¤ ku.
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Preference SQL
The language
basic preference constructors
Pareto/prioritized accumulation
new SQL clause PREFERRING
groupwise preferences
native implementation
Winnow in Preference SQLSELECT * FROM Car
PREFERRING HIGHEST(Year)
CASCADE LOWEST(Price)
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Preference SQL
The language
basic preference constructors
Pareto/prioritized accumulation
new SQL clause PREFERRING
groupwise preferences
native implementation
Winnow in Preference SQLSELECT * FROM Car
PREFERRING HIGHEST(Year)
CASCADE LOWEST(Price)
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Preference SQL
The language
basic preference constructors
Pareto/prioritized accumulation
new SQL clause PREFERRING
groupwise preferences
native implementation
Winnow in Preference SQLSELECT * FROM Car
PREFERRING HIGHEST(Year)
CASCADE LOWEST(Price)
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Algebraic laws [Ch., 2002; Kießling, 2002]
Commutativity of winnow with selection
If the formula
@t1, t2.rαpt2q ^ γpt1, t2qs ñ αpt1q
is valid, then for every r
σαpωγprqq � ωγpσαprqq.
Under the preference relation
pm, y , pq ¡C1 pm1, y 1, p1q � y ¡ y 1 ^ p ¤ p1 _ y ¥ y 1 ^ p p1
the selection σPrice 20K commutes with ωC1 but σPrice¡20K does not.
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Algebraic laws [Ch., 2002; Kießling, 2002]
Commutativity of winnow with selection
If the formula
@t1, t2.rαpt2q ^ γpt1, t2qs ñ αpt1q
is valid, then for every r
σαpωγprqq � ωγpσαprqq.
Under the preference relation
pm, y , pq ¡C1 pm1, y 1, p1q � y ¡ y 1 ^ p ¤ p1 _ y ¥ y 1 ^ p p1
the selection σPrice 20K commutes with ωC1 but σPrice¡20K does not.
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Algebraic laws [Ch., 2002; Kießling, 2002]
Commutativity of winnow with selection
If the formula
@t1, t2.rαpt2q ^ γpt1, t2qs ñ αpt1q
is valid, then for every r
σαpωγprqq � ωγpσαprqq.
Under the preference relation
pm, y , pq ¡C1 pm1, y 1, p1q � y ¡ y 1 ^ p ¤ p1 _ y ¥ y 1 ^ p p1
the selection σPrice 20K commutes with ωC1 but σPrice¡20K does not.
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Semantic query optimization [Ch., 2004]
Using information about integrity constraints to:
eliminate redundant occurrences of winnow.
make more efficient computation of winnow possible.
Eliminating redundancy
Given a set of integrity constraints F , ωC is redundant w.r.t. F iff Fimplies the formula
@t1, t2. Rpt1q ^ Rpt2q ñ t1 �C t2.
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Semantic query optimization [Ch., 2004]
Using information about integrity constraints to:
eliminate redundant occurrences of winnow.
make more efficient computation of winnow possible.
Eliminating redundancy
Given a set of integrity constraints F , ωC is redundant w.r.t. F iff Fimplies the formula
@t1, t2. Rpt1q ^ Rpt2q ñ t1 �C t2.
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Integrity constraints
Constraint-generating dependencies (CGD) [Baudinet et al., 1995]
@t1. . . .@tn. rRpt1q ^ � � � ^ Rptnq ^ γpt1, . . . tnqs ñ γ1pt1, . . . tnq.
CGD entailment
Decidable by reduction to the validity of @-formulas in the constrainttheory (assuming the theory is decidable).
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Integrity constraints
Constraint-generating dependencies (CGD) [Baudinet et al., 1995]
@t1. . . .@tn. rRpt1q ^ � � � ^ Rptnq ^ γpt1, . . . tnqs ñ γ1pt1, . . . tnq.
CGD entailment
Decidable by reduction to the validity of @-formulas in the constrainttheory (assuming the theory is decidable).
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Integrity constraints
Constraint-generating dependencies (CGD) [Baudinet et al., 1995]
@t1. . . .@tn. rRpt1q ^ � � � ^ Rptnq ^ γpt1, . . . tnqs ñ γ1pt1, . . . tnq.
CGD entailment
Decidable by reduction to the validity of @-formulas in the constrainttheory (assuming the theory is decidable).
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Part III
Advanced topics
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Preference modification
Goal
Given a preference relation ¡ and additional preference or indifferenceinformation I, construct a new preference relation ¡1 whose contentsdepend on ¡ and I.
General postulates
fulfillment: the new information I should be completely incorporatedinto ¡1
minimal change: ¡1 should be as close to ¡ as possible
closure:� order-theoretic (SPO, WO) properties of ¡ should be preserved in ¡1
� finiteness or finite representability of ¡ should also be preserved in ¡1
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Preference modification
Goal
Given a preference relation ¡ and additional preference or indifferenceinformation I, construct a new preference relation ¡1 whose contentsdepend on ¡ and I.
General postulates
fulfillment: the new information I should be completely incorporatedinto ¡1
minimal change: ¡1 should be as close to ¡ as possible
closure:� order-theoretic (SPO, WO) properties of ¡ should be preserved in ¡1
� finiteness or finite representability of ¡ should also be preserved in ¡1
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Preference modification
Goal
Given a preference relation ¡ and additional preference or indifferenceinformation I, construct a new preference relation ¡1 whose contentsdepend on ¡ and I.
General postulates
fulfillment: the new information I should be completely incorporatedinto ¡1
minimal change: ¡1 should be as close to ¡ as possible
closure:� order-theoretic (SPO, WO) properties of ¡ should be preserved in ¡1
� finiteness or finite representability of ¡ should also be preserved in ¡1
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Preference revision [Ch., 2007]
Setting
new information: revising preference relation ¡0
composition operator θ: union, prioritized or Pareto composition
composition eliminates (some) preference conflicts
additional assumptions: interval orders
¡1� TC p¡0 θ ¡q to guarantee SPO
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
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Preference revision [Ch., 2007]
Setting
new information: revising preference relation ¡0
composition operator θ: union, prioritized or Pareto composition
composition eliminates (some) preference conflicts
additional assumptions: interval orders
¡1� TC p¡0 θ ¡q to guarantee SPO
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 36 / 46
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Preference revision [Ch., 2007]
Setting
new information: revising preference relation ¡0
composition operator θ: union, prioritized or Pareto composition
composition eliminates (some) preference conflicts
additional assumptions: interval orders
¡1� TC p¡0 θ ¡q to guarantee SPO
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 36 / 46
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Preference revision [Ch., 2007]
Setting
new information: revising preference relation ¡0
composition operator θ: union, prioritized or Pareto composition
composition eliminates (some) preference conflicts
additional assumptions: interval orders
¡1� TC p¡0 θ ¡q to guarantee SPO
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 36 / 46
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Preference revision [Ch., 2007]
Setting
new information: revising preference relation ¡0
composition operator θ: union, prioritized or Pareto composition
composition eliminates (some) preference conflicts
additional assumptions: interval orders
¡1� TC p¡0 θ ¡q to guarantee SPO
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 36 / 46
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Preference contraction [Mindolin, Ch., 2011]
Setting
new information: contractor relation CON
¡1: maximal subset of ¡ disjoint with CON
VW , 2009
VW , 2008
VW , 2007
VW , 2006
VW , 2005
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Preference contraction [Mindolin, Ch., 2011]
Setting
new information: contractor relation CON
¡1: maximal subset of ¡ disjoint with CON
VW , 2009
VW , 2008
VW , 2007
VW , 2006
VW , 2005
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Preference contraction [Mindolin, Ch., 2011]
Setting
new information: contractor relation CON
¡1: maximal subset of ¡ disjoint with CON
VW , 2009
VW , 2008
VW , 2007
VW , 2006
VW , 2005
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Preference contraction [Mindolin, Ch., 2011]
Setting
new information: contractor relation CON
¡1: maximal subset of ¡ disjoint with CON
VW , 2009
VW , 2008
VW , 2007
VW , 2006
VW , 2005
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Preference contraction [Mindolin, Ch., 2011]
Setting
new information: contractor relation CON
¡1: maximal subset of ¡ disjoint with CON
VW , 2009
VW , 2008
VW , 2007
VW , 2006
VW , 2005
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Substitutability [Balke et al., 2006]
Setting
new information: set of indifference pairs
additional preferences are added to achieve object substitutability
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
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Substitutability [Balke et al., 2006]
Setting
new information: set of indifference pairs
additional preferences are added to achieve object substitutability
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
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Substitutability [Balke et al., 2006]
Setting
new information: set of indifference pairs
additional preferences are added to achieve object substitutability
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
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Substitutability [Balke et al., 2006]
Setting
new information: set of indifference pairs
additional preferences are added to achieve object substitutability
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 38 / 46
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Substitutability [Balke et al., 2006]
Setting
new information: set of indifference pairs
additional preferences are added to achieve object substitutability
VW , 2009
VW , 2008
VW , 2007
Kia, 2009
Kia, 2008
Kia, 2007
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Preferences over finite sets
Set preferences
Induced:
X Ï Y � @x P X . Dy P Y . x ¡ y
Aggregate:
X Ï Y � sumApX q ¡ sumApY q
Set preference queries
find the best subsets of a given set
restrictions on cardinality
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Preferences over finite sets
Set preferences
Induced:
X Ï Y � @x P X . Dy P Y . x ¡ y
Aggregate:
X Ï Y � sumApX q ¡ sumApY q
Set preference queries
find the best subsets of a given set
restrictions on cardinality
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 39 / 46
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Preferences over finite sets
Set preferences
Induced:
X Ï Y � @x P X . Dy P Y . x ¡ y
Aggregate:
X Ï Y � sumApX q ¡ sumApY q
Set preference queries
find the best subsets of a given set
restrictions on cardinality
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 39 / 46
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Preferences over set profiles [Zhang, Ch., 2011]
Name Area Rating
Newton Physics 9
Einstein Physics 10
Gargamel Alchemy 0
Preferences
2-element subsets
P1: at most one physicist
P2: higher total rating
P1 more important than P2
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Preferences over set profiles [Zhang, Ch., 2011]
Name Area Rating
Newton Physics 9
Einstein Physics 10
Gargamel Alchemy 0
Preferences
2-element subsets
P1: at most one physicist
P2: higher total rating
P1 more important than P2
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Preferences over set profiles [Zhang, Ch., 2011]
Name Area Rating
Newton Physics 9
Einstein Physics 10
Gargamel Alchemy 0
Preferences
2-element subsets
P1: at most one physicist
P2: higher total rating
P1 more important than P2
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Set profile pF1,F2q
F1pSq � SELECT COUNT(Name) FROM S WHERE Area=’Physics’
F2pSq � SELECT SUM(Rating) FROM S
Set preference relations
X Ï1 Y � F1pX q ¤ 1^ F1pY q ¡ 1
X Ï2 Y � F2pX q ¡ F2pY q
Prioritized composition
X Ï Y � X Ï1 Y _ pY �Ï1 X ^ X Ï2 Y q
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Set profile pF1,F2q
F1pSq � SELECT COUNT(Name) FROM S WHERE Area=’Physics’
F2pSq � SELECT SUM(Rating) FROM S
Set preference relations
X Ï1 Y � F1pX q ¤ 1^ F1pY q ¡ 1
X Ï2 Y � F2pX q ¡ F2pY q
Prioritized composition
X Ï Y � X Ï1 Y _ pY �Ï1 X ^ X Ï2 Y q
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Set profile pF1,F2q
F1pSq � SELECT COUNT(Name) FROM S WHERE Area=’Physics’
F2pSq � SELECT SUM(Rating) FROM S
Set preference relations
X Ï1 Y � F1pX q ¤ 1^ F1pY q ¡ 1
X Ï2 Y � F2pX q ¡ F2pY q
Prioritized composition
X Ï Y � X Ï1 Y _ pY �Ï1 X ^ X Ï2 Y q
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Prospective research topics
Definability
of scoring functions representing preference relations
of CP-nets and other graphical models of preferences
Extrinsic preference relations
Preference relations that are not fully defined by tuple contents:
x ¡ y � Dn1, n2. Dissatisfiedpx , n1q ^ Dissatisfiedpy , n2q ^ n1 n2.
Incomplete preferences
tuple scores and probabilities [Soliman et al., 2007]
uncertain tuple scores
disjunctive preferences: a ¡ b _ a ¡ c
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Prospective research topics
Definability
of scoring functions representing preference relations
of CP-nets and other graphical models of preferences
Extrinsic preference relations
Preference relations that are not fully defined by tuple contents:
x ¡ y � Dn1, n2. Dissatisfiedpx , n1q ^ Dissatisfiedpy , n2q ^ n1 n2.
Incomplete preferences
tuple scores and probabilities [Soliman et al., 2007]
uncertain tuple scores
disjunctive preferences: a ¡ b _ a ¡ c
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Prospective research topics
Definability
of scoring functions representing preference relations
of CP-nets and other graphical models of preferences
Extrinsic preference relations
Preference relations that are not fully defined by tuple contents:
x ¡ y � Dn1, n2. Dissatisfiedpx , n1q ^ Dissatisfiedpy , n2q ^ n1 n2.
Incomplete preferences
tuple scores and probabilities [Soliman et al., 2007]
uncertain tuple scores
disjunctive preferences: a ¡ b _ a ¡ c
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 42 / 46
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Prospective research topics
Definability
of scoring functions representing preference relations
of CP-nets and other graphical models of preferences
Extrinsic preference relations
Preference relations that are not fully defined by tuple contents:
x ¡ y � Dn1, n2. Dissatisfiedpx , n1q ^ Dissatisfiedpy , n2q ^ n1 n2.
Incomplete preferences
tuple scores and probabilities [Soliman et al., 2007]
uncertain tuple scores
disjunctive preferences: a ¡ b _ a ¡ c
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 42 / 46
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Prospective applications
Databases
preference queries as decision components: workflows, event systems
personalization of query results
Multi-agent systems
conflict resolution
negotiating joint preferences and decisions
negotiation preferences
Social media
preference similarity and stability
preference aggregation
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Prospective applications
Databases
preference queries as decision components: workflows, event systems
personalization of query results
Multi-agent systems
conflict resolution
negotiating joint preferences and decisions
negotiation preferences
Social media
preference similarity and stability
preference aggregation
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 43 / 46
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Prospective applications
Databases
preference queries as decision components: workflows, event systems
personalization of query results
Multi-agent systems
conflict resolution
negotiating joint preferences and decisions
negotiation preferences
Social media
preference similarity and stability
preference aggregation
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 43 / 46
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Prospective applications
Databases
preference queries as decision components: workflows, event systems
personalization of query results
Multi-agent systems
conflict resolution
negotiating joint preferences and decisions
negotiation preferences
Social media
preference similarity and stability
preference aggregation
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 43 / 46
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Preference queries vs. Top-K queries
Preference queries Top-K queries
Binary preference relations Scoring functions
Clear declarative reading “Mysterious” formulation
Nondeterminism
No relational data model extension Rank-relations [Li et al., 2005]
Structured data Structured and unstructured data
Manual construction Automatic construction
Preference SQL Mainstream DBMS
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Preference queries vs. Top-K queries
Preference queries Top-K queries
Binary preference relations Scoring functions
Clear declarative reading “Mysterious” formulation
Nondeterminism
No relational data model extension Rank-relations [Li et al., 2005]
Structured data Structured and unstructured data
Manual construction Automatic construction
Preference SQL Mainstream DBMS
Jan Chomicki Preferences, Queries, Logics DBRank, August 29, 2011 44 / 46
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Acknowledgments
Paolo Ciaccia
Parke Godfrey
Jarek Gryz
Werner Kießling
Denis Mindolin
S lawek Staworko
Xi Zhang
Companion paper
Jan Chomicki, Logical Foundations of Preference Queries, DataEngineering Bulletin, 34(2), 2011.
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