#### Transcript PPT - Crystal

Surajit Chaudhuri, Microsoft Research Gautam Das, Microsoft Research Vagelis Hristidis, Florida International University Gerhard Weikum, MPI Informatik Presented by: Kiran Karnam Introduction & Motivation Problem Definition Architecture Ranking Function Implementation Experiments Conclusions & Limitations Many-answers problem Two alternative solutions: Query reformulation Automatic ranking Apply probabilistic model in IR to DB tuple ranking Many answers problem SELECT * FROM REALTOR_DB WHERE CITY=‘SEATTLE’ ; Query reformulation Automatic ranking Specified Attributes city Unspecified Attributes View School District Boat Dock Global Score: Global score which captures the global importance of unspecified attribute values. Eg: VIEW=‘WATERFRONT’ Conditional Score: which captures the strengths of dependencies (or correlations) between specified and unspecified attribute values. Eg: If CITY=‘SEATTLE’ and VIEW=‘WATERFRONT’ Important Rules and Theorem required Bayes’ Rule: p(a/b) = [ p(b/a) p(a) ] / [p(b)] Product Rule: p(a,b/c) = p(a/c) * p(b/a,c) Bayes theorem shows the relation between two conditional probabilities which are the reverse of each other The probability of an event A given an event B depends not only on the relationship between events A and B but on the marginal probability (or "simple probability") of occurrence of each event Document (Tuple) t, Query Q R: Relevant Documents R = D - R: Irrelevant Documents Tuple t is considered as a document Partition t into t(X) and t(Y) t(X) and t(Y) are written as X and Y Derive from initial scoring function until final ranking function is obtained Given a query Q and a tuple t, the X (and Y) values within themselves are assumed to be independent, though dependencies between the X and Y values are allowed If Many Queries Specify Set X of Conditions then there is Preference Correlation between Attributes in X. Global: E.g., If Many Queries ask for Waterfront then p(Waterfront=TRUE) is high. Conditional: E.g., If Many Queries ask for 4-Bedroom Houses in Good School Districts, then p(Bedrooms=4 | SchoolDistrict=`good’), p(SchoolDistrict=`good’ | Bedrooms=4) are high. Final Ranking Formula is Where: p(y|W) = Relative frequency of unspecified attribute ‘y’ given workload ‘W’ p(y|D)= Relative frequency of unspecified attribute ‘y’ given data base ‘D’ p(x|y,W)=Frequency of correlation between x and y in W P(x|y,D)=Frequency of correlation between x and y in D Pre processing ◦ Atomic probability module ◦ Index module Intermediate Knowledge Reference layer Query processing ◦ Scan algorithm ◦ List merge algorithm Computation of modules: p(y | W), p(y | D), p(x | y, W), and p(x | y, D) for all distinct values of x and y. Storing these atomic probabilities as database tables in intermediate knowledge representation layer with appropriate indexes. Computation of index module resulting in conditional and global lists table. CONDITIONAL LISTS Cx: Contains <TID, CondScore> in descending order GLOBAL LISTS Gx: Contains <TID,GlobScore> in descending order Select Tuples that Satisfy the Query Scan and Compute Score for Each Result-Tuple Return Top-K Tuples Scan algorithm is Inefficient Many tuples in the answer set Another approach Pre-compute top-K tuples for all possible queries Still infeasible in practice Trade-off solution Pre-compute ranked lists of tuples for all possible atomic queries At query time, merge ranked lists to get top-K tuples Databases Used ◦ MSN Home Advisor database (http://houseandhome.msn.com/) ◦ Internet Movie Database Software and Hardware: • Microsoft SQL Server2000 RDBMS • P4 2.8-GHz PC, 1 GB RAM • C#, Connected to RDBMS through DAO Quality Experiments Performance Experiments Query: select * from SeattleHomes where City=‘Seattle’ and Bedroom=1; Conditional ranked condos with garages the highest Global failed to recognize importance of the unspecified attribute Garage=‘Y’ User preference of rankings 5 new queries Users were given the top-5 results Compare 2 algorithms ◦ Scan algorithm ◦ List Merge algorithm Execution time of performance algorithms Completely Automated Approach for the Many-Answers Problem which Leverages Data and Workload Statistics and Correlations LIMITATION: Existence of correlations between text and non-text data. Future Work Empty-Answer Problem Handle Plain Text Attributes Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, Gerhard Weikum, Probabilistic Ranking of Database Query Results, VLDB 2004. users.cs.fiu.edu/~vagelis/presentations/ProbRanking.ppt http://crystal.uta.edu/~cse6339/Fall08DBIR.htm http://crystal.uta.edu/~cse6339/Fall09DBIR.htm