1. Prefer: A System for the Efficient Execution
of Multi-parametric Ranked Queries
Vagelis Hristidis
Nick Koudas
Uannis Papakonstantinou
Presented By:
Amrita Tamrakar
09-Mar-2006

2. Contents Introduction Importance/Weights of the attributes
The Efficiency and response time ??
Solution
View Selection
Pipelining Algorithm
Conclusion

3. Prefer
Prefer is a layer on top of commercial database which allows efficient evaluation of multi-parametric databases.
Different attributes may have diff weight
House price and bedrooms have more weight than the age.
Cheap houses may be old and small (so search more)

4. Importance of Attributes
Preference is the keyword
Weights a1 a2 a3 …. an
Preference function
a1A1+a2A2 +….+Anan
Attributes A1 A2 A3 …. An :
tuples

5. Prefer Interface

6. Example
Tid A1 A2 A3
Score 1 10 17 20
17.2 2 11
17.3
Preference = (0.1,0.6,0.3) , Score 10*0.1+17*0.6+20*0.3

7. Efficiency and Response Time
Retrieve the whole Table
Apply the preference function on each tuple
Sort the tuples
Prf fn
Relation
Score
Sort
Top k tuples
……….……….

8. Solution Proposed Ranked Materialized view
10-20 views can have most possible queries
Queries with linear preference function ∑
Preference vector v = {v1,....vn}
Attribute preference

9. Prefer Architechture Best View for the query Materialized Views
Preprocessing Stage
Select Materialized
View
Best View for the query
Materialized
Views
Execute Pipelining
Algorithm
Output
Results
Finding the top k and sorting
View Selection

10. View Selection The union of views will cover the whole space
Outputs view sequence V such that for every query q there is at least one view that covers q
Greedy view selection algorithm for space constraints
Given a set of views { Rv1….Rvn } that covers [0,1]k space, select C views that maximize the number of points in [0,1]k covered.

11. Pipelining Ranked query using
ranked view
T = DeteremineWatermark()
Scan Rv for tuples greater than T
Sort these tuples by fq and mark as processed
Continue for unprocessed tuples till top –k is retrieved v
R v
Rv1
Ranked tuples
Query q
tqi
tq1
R q

12. Example Tid A1 A2 A3 fv(t) fq(t) 1 10 17 20 16.8 17.2 2 11 16.4 17.3 3
Q=(0.1,0.6,0.3) v=(0.2,0.4,0.4)
Tid A1 A2 A3
fv(t)
fq(t) 1 10 17 20
16.8
17.2 2 11
16.4
17.3 3 18 12
15.4
16.1 4 15 8
9.8
10.1 5 9 6
Rv1
The first watermark= 14.26
Sort by fq
Till k tuples are retrieved

14. Conclusion
Introduction of database selection queries with user preferences
Use of multiple DB views
Algorithms to select the best view
Practical application via PREFER