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Probabilistic Ranking of Database Query ResultsPowerPoint Presentation

Probabilistic Ranking of Database Query Results

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### Probabilistic Ranking of Database Query Results

LET US SEE THE

LET US SEE THE

LET US SEE THE

SurajitChaudhuri, Microsoft Research

Gautam Das, Microsoft Research

VagelisHristidis, Florida International University

Gerhard Weikum, MPI Informatik

Presented by:

Ranjanalankarraju

Sindhusatyanarayana

AGENDA

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

LET US SEE THE

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

Introduction and Motivation

REALTOR_DB

PROBLEM DEFINITION- MANY ANSWERS

- SELECT * FROM REALTOR_DB
WHERE CITY=‘SEATTLE’ ;

RESULT OF THIS QUERY: Too Many Answers

PROPOSED SOLUTIONS

- QUERY REFORMULATION TECHNIQUES:
-BY PROMPTING THE USER

- AUTOMATIC RANKING:
-USING GLOBAL AND CONDITIONAL SCORE

LET US SEE THE

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

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

- 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 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

- 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…

- 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

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 THE

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

Adaptation of PIR

- Partition tuple ‘t’ into two parts t(X) and t(Y)
- Replacing t with ‘X’ & ‘Y’

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

- X (and Y) values within themselves are assumed to be independent.
- Dependencies between the X and Y values are allowed

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

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

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…

CONDITIONAL LISTS Cx:

Contains <TID, CondScore> in descending order

GLOBAL LISTS Gx:

Contains <TID,GlobScore> in descending order

IMPLEMENTATION cont…

- Query Processing Component.

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

EXPERIMENTS

- Datasets:
- MSN HomeAdvisor database
- Internet Movie Database(IMDB)

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 Experiments

- User Preference of Rankings:
- Users given top 5 results of rankings for 5 queries
- Ranking preferred by users indicated below:

- Introduction & Motivation
- Problem Definition & Architecture
- Definition of Ranking Function
- Implementation
- Experiments
- Conclusions & Limitations

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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