1. Anthony Okorodudu CSE 6392 2006-4-11
Answering Imprecise Queries over Autonomous Web Databases By Ullas Nambiar and Subbarao Kambhampati
Anthony Okorodudu
CSE 6392

2. Answering Imprecise Queries over Autonomous Web Databases
Outline
Introduction
Overview
AIMQ System
Approach
Attribute Ordering
Query-Tuple Similarity
Conclusion
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

3. Answering Imprecise Queries over Autonomous Web Databases
Introduction
Database query processing models assume user knows what they want and how to formulate query
Users can tell which tuples are of interest to them
Domain and user independent solution for supporting imprecise queries over autonomous Web databases
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

4. Answering Imprecise Queries over Autonomous Web Databases
Overview
Example: Suppose a user wishes to search for sedans priced around $10,000 in a used car database.
Table Schema: CarDB(Make, Model, Year, Price, Location)
Query: CarDB(Model = Camry, Price < 10000)
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

5. Answering Imprecise Queries over Autonomous Web Databases
Overview (continued)
Since Accords are similar, user may also be interested in these
User may also be interested in price slight above $10,000
Basic query processing will not return tuples not specifically satisfying query
User will have to manually issue queries for all “similar” models
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

6. Answering Imprecise Queries over Autonomous Web Databases
Overview (continued)
Automate by telling query processor information about similar models
Difficult to specify domain specific similarity metrics
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

7. Answering Imprecise Queries over Autonomous Web Databases
AIMQ
Remove burden of providing value similarity functions and attribute orders from users
Attempt to reduce human input needed for satisfactory answer
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

8. Answering Imprecise Queries over Autonomous Web Databases
AIMQ Approach
Query: CarDB(Model like Camry, Price like 10000)
Base Query
Qpr: CarDB(Model = Camry, Price = 10000)
Assume non-null resultset
Sample result
Make=Toyota, Model=Camry, Price=10000, Year=2000
Issue queries relaxing any of the attribute bindings
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

9. AIMQ Approach (continued)
Which relaxations will produce more similar tuples?
How to compute similarity between the query and an answer tuple?
Ans(Q) = {x | x ∈ R, Similarity(Q,x) > Tsim}
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

10. Answering Imprecise Queries over Autonomous Web Databases
Attribute Ordering
Tuples most similar to t will differ only in the least important attribute
Identifying least important attribute necessitates an ordering of attributes in terms of their dependence on each other
Estimate importance of attribute by learning the Approximate Functional Dependency (AFD) from a sample of the database
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

11. Answering Imprecise Queries over Autonomous Web Databases
Attribute Ordering
Use Approximate Functional Dependency (AFD) to create attribute dependence graph
Remove cycles and partition into dependent and deciding set
Relax members of dependent sets ahead of deciding set
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

12. Attribute Relaxation Order
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

13. Categorical Value Similarity
Similarity between two values binding a categorical attribute, VSim, is the percentage of common Attribute-Value pairs that are associated to them
Tuple = {Ford, Focus, 15k, 2002}
AV-pair Make=Ford is associated to the AV-pairs Model=Focus, Price=15k, and Year=2002
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

14. Categorical Value Similarity
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

15. Categorical Value Similarity
Measure similarity between two AV-pairs as the similarity shown by their supertuples
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

16. Categorical Value Similarity
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

17. Answering Imprecise Queries over Autonomous Web Databases
Conclusion
AIMQ is a domain independent approach for answering approximate queries over autonomous databases
Attempt to reduce human input needed for satisfactory answers
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

18. Answering Imprecise Queries over Autonomous Web Databases
References
U. Nambiar and S. Kambhampati. Answering Imprecise Queries over Autonomous Web Databases. ICDE Conference.
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases

19. Answering Imprecise Queries over Autonomous Web Databases
Thanks
2006/4/11
Answering Imprecise Queries over Autonomous Web Databases