Probabilistic%20Ranking%20of%20Database%20Query%20Results

1
Probabilistic Ranking of Database Query Results

• Gautam Das, Surajit Chaudhuri, Vagelis Hristidis,
  Gerhard Weikum

Presented by Z.M. Joseph Spring 2006, CSE, UT
Arlington
2
Introduction

• Addresses the Many-Answers problem
• Not a very selective query Has many matching
  tuples
• Thus needs some ranking
• Thus
• Specified attributes all match
• Must look into non-specified attributes

3
Challenge

• How do you select based on non-specified
  attributes?
• Difficult to get correlation information
• Expensive to manage

4
Approach

• Build off Probabilistic Information Retrieval
• Combine
• Global Score
• Contains global importance of unspecified
  attributes
• Conditional Score
• Captures strength of correlation between
  unspecified and specified attributes
• Preprocessing at Intermediate Knowledge
  Representation Layer

5
Recall from PIR

• We already know that for a tuple t
• t can be broken down as
• X As the set of specified attributes
• Y The list of unspecified attributes
• R is the ideal set of result tuples
• D is a single database table (approximated to R)

6
Structured Data

• Simplifies to
• This automatically increases probability for
  unspecified attributes that occur more in the
  ideal tuple set R

7
Limited Independence Assumptions

• Possible to capture dependencies and correlations
  from structured data
• Efficient approach
• X and Y values within themselves are independent
  of each other
• Allows derivation of
• This assumption may not always be correct!

8
Workload-Based R Estimation

• In order to use these techniques, the ideal
  result set R must be known.
• Use statistics gathered from the workload
• View the workload as a set of tuples containing
  each query and the specified attributes
• Thus can replace P(yR) with P(yX,W)
• Properties of R can be obtained by examining the
  workload for queries that retrieved X in the past

9
Workload-Based R Estimation

• Thus the ranking function is
• Does not contain R
• Quantities are all atomic and can be computed
• First part is global, second part is conditional
• Can use association rules for , etc.
• These values stored in intermediate knowledge
  representation layer

10
Implementation

• Atomic Probabilities Module stores atomic
  quantities in the intermediate knowledge
  representation layer
• Index Module Uses inputs and association rules
  to create global and conditional scores
• Scan Algorithm Selects tuples that satisfy the
  condition and then finds the ranking based on the
  scores
• List Merge Algorithm Alternate to scanning

11
Conclusion

• Gives a ranking for the Many-Answer problem by
  factoring in unspecified attributes
• Automated
• Makes use of workload statistics and correlations
• Can still be adjusted by users and/or domain
  experts
• Can use user feedback as well