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Probabilistic Ranking of Database Query Results. Surajit Chaudhuri , Microsoft Research Gautam Das, Microsoft Research Vagelis Hristidis , Florida International University Gerhard Weikum , MPI Informatik Presented by: Ranjan alankar raju Sindhu satyanarayana. AGENDA.

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Probabilistic ranking of database query results

Probabilistic Ranking of Database Query Results

SurajitChaudhuri, Microsoft Research

Gautam Das, Microsoft Research

VagelisHristidis, Florida International University

Gerhard Weikum, MPI Informatik

Presented by:

Ranjanalankarraju

Sindhusatyanarayana


Agenda
AGENDA

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Let us see the
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations



Problem definition many answers
PROBLEM DEFINITION- MANY ANSWERS

  • SELECT * FROM REALTOR_DB

    WHERE CITY=‘SEATTLE’ ;

    RESULT OF THIS QUERY: Too Many Answers


Proposed solutions
PROPOSED SOLUTIONS

  • QUERY REFORMULATION TECHNIQUES:

    -BY PROMPTING THE USER

  • AUTOMATIC RANKING:

    -USING GLOBAL AND CONDITIONAL SCORE


Let us see the1
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Definitions and symbols
DEFINITIONS AND SYMBOLS

  • What are Specified Attributes (Denoted as ‘X’)

  • City

  • What are Unspecified Attributes (Denoted as ‘Y’)

  • View

  • Price

  • SchoolDistrict

  • BoatDock


Proposed ranking function
PROPOSED RANKING FUNCTION

  • Global Score : Global importance of unspecified attributes

    Eg: VIEW=‘WATERFRONT’

  • Conditional Score: Correlations between specified and unspecified attributes

    Eg: If CITY=‘SEATTLE’ and VIEW=‘WATERFRONT’

    Will BOATDOCK=‘YES’ interest him?



Ranking functions rules theorems for pir
RANKING FUNCTIONSRules & Theorems For PIR

  • 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 example
BAYES’ THEOREM EXAMPLE

  • 1% of the population has X disease.. A screening test accurately detects the disease for 90% of people with it. The test also indicates the disease for 15% of the people without it ( the false positives). Suppose a person screened for the disease tests positive. What is the probability they have it?


Bayes theorem cont
BAYES’ THEOREM Cont…

  • Interpretation and Assumption:

    D - Event that person has disease

    T- Test is Positive

  • Given:

    p(D)= 1% p(D|T)=?

    p(T|D) = 90 %

    p(T|D’)=15%


Tree structure interpretation
Tree structure Interpretation

Four Cases

1. (D n T)-Has disease and test +ve. 3. (D’ n T)- No disease and test +ve. 2. (D n T’)-Has disease and test –ve. 4. (D’ n T’)- No disease and test –ve.

1

D’

D

T

T

T’

T’


Let us see the2
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Rules theorems for pir cont
Rules & Theorems For PIR cont…

t-Tuple (Document)

R-Relevant Documents

R- Irrelevant Documents


Adaptation of pir
Adaptation of PIR

  • Partition tuple ‘t’ into two parts t(X) and t(Y)

  • Replacing t with ‘X’ & ‘Y’


Adaptation of pir cont
Adaptation of PIR cont…

  • QUERY SPECIFIED BY USER:

    Select * From Realtor_db

    where City=‘Seattle’ and Price=‘High’;

  • FINAL RANKING:

  • Waterfront Views

  • Greenbelt Views

  • Street Views


Limited independence assumption
Limited Independence Assumption

  • X (and Y) values within themselves are assumed to be independent.

  • Dependencies between the X and Y values are allowed


Eliminating r
Eliminating R

Incoming Query:

Select * from Realtor_db where City=‘Seattle’;


Workload based estimation
Workload-Based Estimation

FINAL RANKING FORMULA

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



Let us see the3
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Implementation
IMPLEMENTATION

  • Preprocessing:

    1. 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.

    2. Storing these atomic probabilities as database tables in intermediate knowledge representation layer with appropriate indexes.

    3.Computation of index module resulting in conditional and global lists table.


Implementation cont
IMPLEMENTATION cont…

CONDITIONAL LISTS Cx:

Contains <TID, CondScore> in descending order

GLOBAL LISTS Gx:

Contains <TID,GlobScore> in descending order





Implementation cont2
IMPLEMENTATION cont…

  • Query Processing Component.



Let us see the4
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Experiments
EXPERIMENTS

  • Datasets:

  • MSN HomeAdvisor database

  • Internet Movie Database(IMDB)


Quality experiments
Quality Experiments

  • Examples of Ranking Results:

    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’


Quality experiments1
Quality Experiments

  • User Preference of Rankings:

  • Users given top 5 results of rankings for 5 queries

  • Ranking preferred by users indicated below:


Let us see the5
LET US SEE THE

  • Introduction & Motivation

  • Problem Definition & Architecture

  • Definition of Ranking Function

  • Implementation

  • Experiments

  • Conclusions & Limitations


Conclusion limitation
CONCLUSION & LIMITATION

CONCLUSION:

Automated approach leverages data and workload statistics and correlations.

LIMITATION:

Existence of correlations between text and non-text data.


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