davide mottin, senjuti basu roy, alice marascu, yannis velegrakis, themis palpanas, gautam das a...
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Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin
A Probabilistic Optimization Framework for the Empty-Answer Problem Davide MottinAlice Marascu, Senjuti Basu Roy, Gautam Das, Themis Palpanas, Yannis Velegrakis
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 2
Empty-Answer Problem
CARDB
query = Alarm, DSL, Manual
{}
No answer
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 3
Dealing with the Empty Answer Problem
Ranking results based on user preferencesIR [Baeza11] and database solutions [Chaudhuri04]
Query relaxationModify some of the query conditions [Mishra09]
(-) Suggests all the modification together(-) Does not take user feedback into account
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 4
Our Solution: Interactive Query Relaxation
Suggests one relaxation at a timeTakes user feedback into accountModels user preferencesOptimization centric relaxation suggestions
User centric (effort, relevance)System-centric (profit)
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 5
ChallengesExponential number of relaxationsModeling user preferencesSystem encoding of different objective functions
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 6
Our Approach
A probabilistic optimization framework• Based on probability that user says yes to relaxation Q’ of
query Q
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin
The Probabilistic Framework
Probability of accepting relaxation Q’ of Q belief of user that an answer will be found in the database: Priorlikelihood the user will like the answers of relaxed query: Pref
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin
The Probabilistic Framework
Probability to reject a relaxation
Cost for a relaxation
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 9
Different objective functionsMaximize profit
Pref: favors solutions with highest values of individual tuplesa static function
Maximize answer relevancePref: favors solutions with most relevant tuples to original query
Semi-dynamic function (computed only once with the user query
Minimize user effortPref: favors solutions with least number of user interactions
fully dynamic function (changes at every relaxation)
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 10
Minimum Effort Objective
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 11
Min-Effort Relaxation Tree
0 0
0.3 0.71
1 10 0
1 2
1
0.3 0.7
Query : (Alarm, DSL, Manual) Relaxation nodes
Choice nodes
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 12
Algorithmic Contributions
Exact algorithm (FastOpt): Upper and lower bound for each nodePruning can be enabled for this algorithm
Approximate algorithm (CDR): Nodes cost approximated by probability distributionRelaxation nodes: min/max distribution of CostChoice nodes: sum distribution of CostApproximated by computing the convolution cost
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 13
Fast Solution (FastOpt)
Idea: prune non-optimal relaxations in advance• Upper and lower bound of cost function• Prune branches using upper/lower bounds
reasoning
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 14
FastOpt Algorithm (Min-Effort)
Prune!!!
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 15
Experimental SetupDatasets:
US Home dataset: 38k tuples, 18 attributesCar dataset: 100k tuples, 31 attributesSyntetic datasets: 20k to 500k tuples
Baseline algorithms: Previous works: top-k, query-refinement, rankingRandom relaxationGreedy: choose the first non empy otherwise random
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 16
User Study Set up
1. Interactive vs non-interactive• Measure user satisfaction with our interactive
approach vs relax at-once approaches• 100 Amazon Turk users, 5 queries each
2. Objective functions effectiveness• Compare proposed relaxations with objective
function goals (max profit, min effort, max user relevance)
• Three tasks• 100 users, 5 queries
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 17
Experimental Results Highlights
Scalability results:FastOpt (Exact): timely exact answers for small queriesCDR (Approximate)
real time answers for queries size 10results close to optimal
User study resultsInteractive methods preferred over non-interactiveObjective functions correctly achieve their optimization goals
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 18
User Effort Comparison
• CDR close to optimal• Random and Greedy
produce 1.5 more relaxations
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
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Query TimeExponential behaviour
Efficient for small queries
1.4 sec for query size 10!!!
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 20
User Study
Users prefer interactive systems to relaxations all at onceBetter quality answers
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 21
ConclusionsIntroduce novel principled, user-centric and interactive approach for the empty-answer problemPropose exact and approximate algorithmsDemonstrate scalability of proposed techniques with database and query sizeShow effectiveness of the different objective functions Verify quality of the answers and superior usability of our interactive approach
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 23
Goal comparison
Objective functions achieve their goalsDynamic and Semi-Dynamic very similar in performance
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 24
Approximate Solution (CDR)Idea: use cost distribution instead of actual cost. 1. b-size histogram in each node2. Construct the tree first L levels3. Expand the branch with the biggest probability of
being the optimal
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Choose the Branch to Expand1. compute the probability that the cost is smaller than
the siblings 2. choose the son with the highest probability
Pr(n1<n2) = 0.6 n1 n2 Pr(n2<n1) = 0.4
Expand this!
Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das
A Probabilistic Optimization Framework for the Empty-Answer ProblemDavide Mottin 26
Bibliography
[Mishra09] C. Mishra and N. Koudas, “Interactive query refinement,” in EDBT,2009.[Roy08] S. Basu Roy, H. Wang, G. Das, U. Nambiar, and M. Mohania, “Minimum-effort driven dynamic faceted search in structured databases,” in CIKM, 2008.[Chadhuri04] S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum, “Probabilistic ranking of database query results,” in VLDB, 2004.[Baeza11] R. A. Baeza-Yates and B. A. Ribeiro-Neto, Modern Information Retrieval, 2011.