Answering Imprecise Queries over Autonomous Web DatabasesBy Ullas Nambiar and Subbarao Kambhampati
Anthony OkoroduduCSE 63922006-4-11
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Outline Introduction Overview AIMQ System Approach Attribute Ordering Query-Tuple Similarity Conclusion
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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
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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)
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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
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Overview (continued) Automate by telling query
processor information about similar models
Difficult to specify domain specific similarity metrics
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AIMQ Remove burden of providing value
similarity functions and attribute orders from users
Attempt to reduce human input needed for satisfactory answer
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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
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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}
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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
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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
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Attribute Relaxation Order
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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
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Categorical Value Similarity
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Categorical Value Similarity Measure similarity between two
AV-pairs as the similarity shown by their supertuples
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Categorical Value Similarity
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Conclusion AIMQ is a domain independent
approach for answering approximate queries over autonomous databases
Attempt to reduce human input needed for satisfactory answers
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References U. Nambiar and S. Kambhampati.
Answering Imprecise Queries over Autonomous Web Databases. ICDE Conference.
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Thanks
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