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 Thus:
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(y|R) with P(y|X,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