Preference Queries with Aggregate Constraints · LH, FC, 990 e Expo, 3*, 360 e BMW, L, 210 e...
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Preference Queries with Aggregate Constraints Markus Endres University of Augsburg Sion 2007 Preference Queries with Aggregate Constraints Sion 2007 1 / 17
Transcript of Preference Queries with Aggregate Constraints · LH, FC, 990 e Expo, 3*, 360 e BMW, L, 210 e...
Preference Querieswith Aggregate Constraints
Markus Endres
University of Augsburg
Sion 2007
Preference Queries with Aggregate Constraints Sion 2007 1 / 17
Problem DescriptionTravel Preferences
Szenario: Vacation Planning - One week in Vienna
Preferences and wishes
Target:
Fulfill all preferences and keep a price ≤ 1050 e.
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Problem DescriptionAn Example
Flight Hotel CarQA, BC, 680 e Gran, 2*, 125 e VW, C, 120 eKLM, BC, 810 e Ritz, 4*, 250 e Audi, L, 175 eLH, FC, 990 e Expo, 3*, 360 e BMW, L, 210 e
Target:
Fulfill all preferences and keep a price ≤ 1050 e.
Preference Queries with Aggregate Constraints Sion 2007 3 / 17
Problem DescriptionAn Example
Flight Hotel CarQA, BC, 680 e Gran, 2*, 125 e VW, C, 120 eKLM, BC, 810 e Ritz, 4*, 250 e Audi, L, 175 eLH, FC, 990 e Expo, 3*, 360 e BMW, L, 210 e
Argh, very bad!
My preferences are fulfilled to the very best but the price of 1290 eexceeds my constraint of 1050 e.
Preference Queries with Aggregate Constraints Sion 2007 3 / 17
Problem DescriptionAn Example
Flight Hotel CarQA, BC, 680 e Gran, 2*, 125 e VW, C, 120 eKLM, BC, 810 e Ritz, 4*, 250 e Audi, L, 175 eLH, FC, 990 e Expo, 3*, 360 e BMW, L, 210 e
Yeah, fantastic!
The price of 1050 e exactly matches my budget and my preferences arealmost fulfilled.
Preference Queries with Aggregate Constraints Sion 2007 3 / 17
Big Question
How to retrieve allFlight - Hotel - RentalCarcombinations with best
possible completion of alluser preferencesin particular the
hard price constraint?
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The BruteForce Algorithm
Brute force
Compute cartesian product F × H × C
Determine all combinations with aggregate price ≤ 1050 e
Fulfill all user preferences best possible
Disadvantages
Damned time consuming
Unfeasible for E-Commerce
Empty result effect
Preferences maybe not satisfied
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Quality AssignmentAnd Further Algorithms
Useful but not feasible
Apply preference selection first
Problem: Empty result set
Quality assignment for preferences
perfect hit −→good hit −→bad hit −→
Flight Hotel CarQA, BC, 680 e, Gran, 2*, 125 e, VW, C, 120 e,KLM, BC, 810 e, Ritz, 4*, 250 e, Audi, L, 175 e,LH, FC, 990 e, Expo, 3*, 360 e, BMW, L, 210 e,
Preference Queries with Aggregate Constraints Sion 2007 6 / 17
Quality AssignmentAnd Further Algorithms
Useful but not feasible
Apply preference selection first
Problem: Empty result set
Quality assignment for preferences
perfect hit −→good hit −→bad hit −→
Flight Hotel CarQA, BC, 680 e, Gran, 2*, 125 e, VW, C, 120 e,KLM, BC, 810 e, Ritz, 4*, 250 e, Audi, L, 175 e,LH, FC, 990 e, Expo, 3*, 360 e, BMW, L, 210 e,
Preference Queries with Aggregate Constraints Sion 2007 6 / 17
Why Smilies?Makes this sense?
Preference Queries with Aggregate Constraints Sion 2007 7 / 17
The DOMINANCE AlgorithmAn Improvement
Dominance Criterion
Given items i and j such that
i .price ≥ j .price ∧ i .smiley < j .smiley
then there exists an optimal solution without item i .
Flight Hotel CarQA, BC, 680 e, Gran, 2*, 125 e, VW, C, 120 e,KLM, BC, 810 e, Ritz, 4*, 250 e, ///////Audi,////L,//////175/////e,////////LH,//////FC,//////990/////e,/// Expo, 3*, 360 e, /////////BMW,////L,//////210////e,////
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The DOMINANCE AlgorithmThe Algorithm
Assign preference qualities to items
Apply dominance criterion → much less items
Build all combinations with aggregate price ≤ 1050 e
Find best combinations concerning the user preferences
Flight Hotel CarQA, BC, 680 e, Gran, 2*, 125 e, VW, C, 120 e,KLM, BC, 810 e, Ritz, 4*, 250 e,
Expo, 3*, 360 e,
Preference Queries with Aggregate Constraints Sion 2007 9 / 17
The HEURISTICS AlgorithmAgain an Improvement
Good old sorting ...
Sorting by price after applying the dominance criterion sorts theundominated items by increasing smiley, too.
Flight Hotel CarQA, BC, 680 e, Gran, 2*, 125 e, VW, C, 120 e,KLM, BC, 810 e, Ritz, 4*, 250 e, ///////Audi,////L,//////175/////e,////////LH,//////FC,//////990/////e,/// Expo, 3*, 360 e, /////////BMW,////L,//////210////e,////
Preference Queries with Aggregate Constraints Sion 2007 10 / 17
The HEURISTICS AlgorithmThe Algorithm
Assign preference qualities to items
Apply dominance criterion → much less items
Build all combinations with aggregate price ≤ 1050 e
Sort undominated items by increasing price.
Guess a solution ≤ 1050 e
Find further / better preference solutions in a neighborhood
Preference Queries with Aggregate Constraints Sion 2007 11 / 17
The HEURISTICS AlgorithmThe Algorithm
Assign preference qualities to items
Apply dominance criterion → much less items
Build all combinations with aggregate price ≤ 1050 e
Sort undominated items by increasing price.
Guess a solution ≤ 1050 e
Find further / better preference solutions in a neighborhood
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The HEURISTICS AlgorithmAn Example
Flight Hotel CarBA, 180 e, Loyd, 65 e, Fiat, 99 e,FD, 380 e, Palm, 95 e, Opel, 105 e,QA, 680 e, Gran, 125 e, VW, 120 e, 925 eKLM, 810 e, Ritz, 250 e, Audi, 175 e,LH, 990 e, Expo, 360 e, BMW, 210 e,LH, 1090 e, Ritz, 460 e, Seat, 250 e,
Preference Queries with Aggregate Constraints Sion 2007 12 / 17
The HEURISTICS AlgorithmAn Example
Flight Hotel CarBA, 180 e, Loyd, 65 e, Fiat, 99 e,FD, 380 e, Palm, 95 e, Opel, 105 e,QA, 680 e, Gran, 125 e, VW, 120 e, 925 eKLM, 810 e, Ritz, 250 e, Audi, 175 e, 980 eLH, 990 e, Expo, 360 e, BMW, 210 e,LH, 1090 e, Ritz, 460 e, Seat, 250 e,
Preference Queries with Aggregate Constraints Sion 2007 12 / 17
The HEURISTICS AlgorithmAn Example
Flight Hotel CarBA, 180 e, Loyd, 65 e, Fiat, 99 e,FD, 380 e, Palm, 95 e, Opel, 105 e,QA, 680 e, Gran, 125 e, VW, 120 e, 1050 eKLM, 810 e, Ritz, 250 e, Audi, 175 e,LH, 990 e, Expo, 360 e, BMW, 210 e,LH, 1090 e, Ritz, 460 e, Seat, 250 e,
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Evaluation
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EvaluationPerformance Tests
3 relations
correlated data
up to 2000 items
random priceconstraint
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EvaluationNumber of Solutions
correct
maybeincomplete
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Summary and Outlook
Quality assignment →DOMINANCE & HEURISTICS
Performance enhancement
E-Commerce
Further aggregate constraints
Find other optimization techniques
Planning process
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Summary and Outlook
Quality assignment →DOMINANCE & HEURISTICS
Performance enhancement
E-Commerce
Further aggregate constraints
Find other optimization techniques
Planning process
Preference Queries with Aggregate Constraints Sion 2007 16 / 17
Questions???
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