Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Alice 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 Problem Davide Mottin CAR DB CAR DB query = Alarm, DSL, Manual {} No answer 2

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin 3 Ranking results based on user preferences IR [Baeza11] and database solutions [Chaudhuri04] Query relaxation Modify 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 Problem Davide Mottin Suggests one relaxation at a time Takes user feedback into account Models user preferences Optimization centric relaxation suggestions User centric (effort, relevance) System-centric (profit) 4

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Exponential number of relaxations Modeling user preferences System encoding of different objective functions 5

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin A probabilistic optimization framework Based on probability that user says yes to relaxation Q’ of query Q 6

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Probability of accepting relaxation Q’ of Q belief of user that an answer will be found in the database: Prior likelihood 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 Problem Davide Mottin 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 Problem Davide Mottin Maximize profit Pref: favors solutions with highest values of individual tuples a static function Maximize answer relevance Pref: favors solutions with most relevant tuples to original query Semi-dynamic function (computed only once with the user query Minimize user effort Pref: favors solutions with least number of user interactions fully dynamic function (changes at every relaxation) 9

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Minimum Effort Objective 10

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin 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 Problem Davide Mottin Exact algorithm (FastOpt): Upper and lower bound for each node Pruning can be enabled for this algorithm Approximate algorithm (CDR): Nodes cost approximated by probability distribution Relaxation nodes: min/max distribution of Cost Choice nodes: sum distribution of Cost Approximated by computing the convolution cost 12

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Idea: prune non-optimal relaxations in advance Upper and lower bound of cost function Prune branches using upper/lower bounds reasoning 13

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Prune!!! 14

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Datasets: US Home dataset: 38k tuples, 18 attributes Car dataset: 100k tuples, 31 attributes Syntetic datasets: 20k to 500k tuples Baseline algorithms: Previous works: top-k, query-refinement, ranking Random relaxation Greedy: choose the first non empy otherwise random 15

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin 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 16

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Scalability results: FastOpt (Exact): timely exact answers for small queries CDR (Approximate) real time answers for queries size 10 results close to optimal User study results Interactive methods preferred over non-interactive Objective functions correctly achieve their optimization goals 17

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin CDR close to optimal Random and Greedy produce 1.5 more relaxations 18

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Exponential behaviour Efficient for small queries 1.4 sec for query size 10!!! 19

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin 20 Users prefer interactive systems to relaxations all at once Better quality answers

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Introduce novel principled, user-centric and interactive approach for the empty-answer problem Propose exact and approximate algorithms Demonstrate scalability of proposed techniques with database and query size Show effectiveness of the different objective functions Verify quality of the answers and superior usability of our interactive approach 21

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Objective functions achieve their goals Dynamic and Semi-Dynamic very similar in performance 23

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin Idea: use cost distribution instead of actual cost. 1.b-size histogram in each node 2.Construct the tree first L levels 3.Expand the branch with the biggest probability of being the optimal 24

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin 1.compute the probability that the cost is smaller than the siblings 2.choose the son with the highest probability Pr(n1<n2) = 0.6n1n2 Pr(n2<n1) = 0.4 Expand this! 25

Davide Mottin, Senjuti Basu Roy, Alice Marascu, Yannis Velegrakis, Themis Palpanas, Gautam Das A Probabilistic Optimization Framework for the Empty-Answer Problem Davide Mottin [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, [Chadhuri04] S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum, “Probabilistic ranking of database query results,” in VLDB, [Baeza11] R. A. Baeza-Yates and B. A. Ribeiro-Neto, Modern Information Retrieval,