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Anthony Okorodudu CSE 6392 2006-4-11

Answering Imprecise Queries over Autonomous Web Databases By Ullas Nambiar and Subbarao Kambhampati. Anthony Okorodudu CSE 6392 2006-4-11. Outline. Introduction Overview AIMQ System Approach Attribute Ordering Query-Tuple Similarity Conclusion. Introduction.

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Anthony Okorodudu CSE 6392 2006-4-11

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

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

  3. 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 Answering Imprecise Queries over Autonomous Web Databases

  4. 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) Answering Imprecise Queries over Autonomous Web Databases

  5. 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 Answering Imprecise Queries over Autonomous Web Databases

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

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

  8. 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 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} Answering Imprecise Queries over Autonomous Web Databases

  10. 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 Answering Imprecise Queries over Autonomous Web Databases

  11. 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 Answering Imprecise Queries over Autonomous Web Databases

  12. Attribute Relaxation Order 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 Answering Imprecise Queries over Autonomous Web Databases

  14. Categorical Value Similarity Answering Imprecise Queries over Autonomous Web Databases

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

  16. Categorical Value Similarity Answering Imprecise Queries over Autonomous Web Databases

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

  18. References • U. Nambiar and S. Kambhampati. Answering Imprecise Queries over Autonomous Web Databases. ICDE Conference. Answering Imprecise Queries over Autonomous Web Databases

  19. Thanks Answering Imprecise Queries over Autonomous Web Databases

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