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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 Weimin He [email protected] Outline. Motivation Problem Definition

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probabilistic ranking of database query results

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 Weimin He

[email protected]

outline
Outline

Motivation

Problem Definition

System Architecture

Construction of Ranking Function

Implementation

Experiments

Conclusion and open problems

Weimin He [email protected]

motivating example
Motivating example

Realtor DB:

Table D=(TID, Price , City, Bedrooms, Bathrooms, LivingArea, SchoolDistrict, View, Pool, Garage, BoatDock)

SQL query:

Select * From D

Where City=Seattle AND View=Waterfront

Weimin He [email protected]

motivation
Motivation
  • Many-answers problem
  • Two alternative solutions:

Query reformulation

Automatic ranking

  • Apply probabilistic model in IR to DB tuple ranking

Weimin He [email protected]

problem definition
Problem Definition

Given a database table D with n tuples {t1, …, tn} over a set of m categorical attributes A = {A1, …, Am}

and a query Q: SELECT * FROM D

WHERE

X1=x1 AND … AND Xs=xs

where each Xi is an attribute from A and xi is a value in its domain.

The set of attributes X ={X1, …, Xs} is known as the set of attributes specified by the query, while the set Y = A – X is known as the set of unspecified attributes

Let be the answer set of Q

How to rank tuples in S and return top-k tuples to the user ?

Weimin He [email protected]

intuition for ranking function
Intuition for Ranking Function
  • Select * From D Where City=“Seattle” And View=“Waterfront”

Score of a Result Tuple t depends on

  • Global Score: Global Importance of Unspecified Attribute Values
    • E.g., Homes with good school districts are globally desirable
  • Conditional Score: Correlations between Specified and Unspecified Attribute Values
    • E.g., Waterfront  BoatDock

Weimin He [email protected]

probabilistic model in ir
Probabilistic Model in IR
  • Bayes’ Rule
  • Product Rule
  • Document t, Query QR: Relevant document setR = D - R: Irrelevant document set

Weimin He [email protected]

adaptation of pir to db
Adaptation of PIR to DB
  • 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

Weimin He [email protected]

limited independence assumptions
Limited Independence Assumptions
  • 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

Weimin He [email protected]

workload based estimation of
Workload-based Estimation of

Assume a collection of “past” queries existed in system

Workload W is represented as a set of “tuples”

Given query Q and specified attribute set X, approximate R as all query “tuples” in W that also request for X

All properties of the set of relevant tuple set R can be obtained by only examining the subset of the workload that caontains queries that also request for X

Weimin He [email protected]

pre computing atomic probabilities in ranking function
Pre-computing Atomic Probabilities in Ranking Function

Relative frequency in W

Relative frequency in D

(#of tuples in W that conatains x, y)/total # of tuples in W

(#of tuples in D that conatains x, y)/total # of tuples in D

Weimin He [email protected]

example for computing atomic probabilities
Example for Computing Atomic Probabilities
  • Select * From D Where City=“Seattle” And View=“Waterfront”
  • Y={SchoolDistrict, BoatDock, …}
  • D=10,000 W=1000
  • W{excellent}=10
  • W{waterfront &yes}=5
  • p(excellent|W)=10/1000=0.1
  • p(excellent|D)=10/10,000=0.01
  • p(waterfront|yes,W)=5/1000=0.005
  • p(waterfront|yes,D)=5/10,000=0.0005

Weimin He [email protected]

indexing atomic probabilities
Indexing Atomic Probabilities

{AttName, AttVal, Prob}

B+ tree index on (AttName, AttVal)

{AttName, AttVal, Prob}

B+ tree index on (AttName, AttVal)

{AttNameLeft, AttValLeft, AttNameRight, AttValRight, Prob}

B+ tree index on (AttNameLeft, AttValLeft, AttNameRight, AttValRight)

{AttNameLeft, AttValLeft, AttNameRight, AttValRight, Prob}

B+ tree index on (AttNameLeft, AttValLeft, AttNameRight, AttValRight)

Weimin He [email protected]

scan algorithm
Scan Algorithm

Preprocessing - Atomic Probabilities Module

  • Computes and Indexes the Quantities P(y | W), P(y | D), P(x | y, W), and P(x | y, D) for All Distinct Values x and y

Execution

  • Select Tuples that Satisfy the Query
  • Scan and Compute Score for Each Result-Tuple
  • Return Top-K Tuples

Weimin He [email protected]

beyond scan algorithm
Beyond Scan Algorithm
  • Scan algorithm is Inefficient

Many tuples in the answer set

  • Another extreme

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

Weimin He [email protected]

two kinds of ranked list
Two kinds of Ranked List
  • CondList Cx

{AttName, AttVal, TID, CondScore}

B+ tree index on (AttName, AttVal, CondScore)

  • GlobList Gx

{AttName, AttVal, TID, GlobScore}

B+ tree index on (AttName, AttVal, GlobScore)

Weimin He [email protected]

experimental setup
Experimental Setup
  • Datasets:
    • MSR HomeAdvisor Seattle (http://houseandhome.msn.com/)
    • Internet Movie Database (http://www.imdb.com)
  • Software and Hardware:
    • Microsoft SQL Server2000 RDBMS
    • P4 2.8-GHz PC, 1 GB RAM
    • C#, Connected to RDBMS through DAO

Weimin He [email protected]

quality experiments
Quality Experiments
  • Conducted on Seattle Homes and Movies tables
  • Collect a workload from users
  • Compare Conditional Ranking Method in the paper with the Global Method [CIDR03]

Weimin He [email protected]

quality experiment average precision
Quality Experiment-Average Precision
  • For each query Qi , generate a set Hi of 30 tuples likely to contain a good mix of relevant and irrelevant tuples
  • Let each user mark 10 tuples in Hi as most relevant to Qi
  • Measure how closely the 10 tuples marked by the user match the 10 tuples returned by each algorithm

Weimin He [email protected]

quality experiment fraction of users preferring each algorithm
Quality Experiment- Fraction of Users Preferring Each Algorithm
  • 5 new queries
  • Users were given the top-5 results

Weimin He [email protected]

performance experiments
Performance Experiments
  • Datasets
  • Compare 2 Algorithms:
    • Scan algorithm
    • List Merge algorithm

Weimin He [email protected]

conclusion and open problems
Conclusion and Open Problems
  • Automatic ranking for many-answers
  • Adaptation of PIR to DB
  • Mutiple-table query
  • Non-categorical attributes

Weimin He [email protected]

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