anthony okorodudu cse 6392 2006-4-11

Answering Imprecise Queries over Autonomous Web DatabasesBy Ullas Nambiar and Subbarao Kambhampati

Anthony OkoroduduCSE 63922006-4-11

2006/4/11 Answering Imprecise Queries over Autonomous Web Databases

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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

anthony okorodudu cse 6392 2006-4-11

Documents

setanswering imprecise

issue queries

attribute orders

categorical attribute

price slight

similar tuples

similar modelsdifficult

value similarity functions