Supporting Queries with Imprecise Constraints

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Supporting Queries with Imprecise Constraints

• Ullas Nambiar
• Dept. of Computer Science
• University of California, Davis

Subbarao Kambhampati Dept. of Computer
Science Arizona State University
18th July, AAAI -06, Boston, USA
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Dichotomy in Query Processing

• IR Systems
• User has an idea of what she wants
• User query captures the need to some degree
• Answers ranked by degree of relevance

• Databases
• User knows what she wants
• User query completely expresses the need
• Answers exactly matching query constraints

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Why Support Imprecise Queries ?
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Others are following
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What does Supporting Imprecise Queries Mean?

• The Problem Given a conjunctive query Q over a
  relation R, find a set of tuples that will be
  considered relevant by the user.
• Ans(Q) xx ? R, Rel(xQ,U) gtc
• Constraints
• Minimal burden on the end user
• No changes to existing database
• Domain independent

Relevance Assessment
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Assessing Relevance Function Rel(xQ,U)

• We looked at a variety of non-intrusive relevance
  assessment methods
• Basic idea is to learn the relevance function for
  user population rather than single users
• Methods
• From the analysis of the (sample) data itself
• Allows us to understand the relative importance
  of attributes, and the similarity between the
  values of an attribute
• ICDE 2006WWW 2005 poster
• From the analysis of query logs
• Allows us to identify related queries, and then
  throw in their answers
• WIDM 2003 WebDB 2004
• From co-click patterns
• Allows us to identify similarity based on user
  click pattern
• Under Review

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Our Solution AIMQ
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The AIMQ Approach
For the special case of empty query, we start
with a relaxation that uses AFD analysis
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An Illustrative Example

• Relation- CarDB(Make, Model, Price, Year)
• Imprecise query
• Q - CarDB(Model like Camry, Price like
  10k)
• Base query
• Qpr - CarDB(Model Camry, Price 10k)
• Base set Abs
• Make Toyota, Model Camry, Price
  10k, Year 2000
• Make Toyota, Model Camry, Price
  10k, Year 2001

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Obtaining Extended Set

• Problem Given base set, find tuples from
  database similar to tuples in base set.
• Solution
• Consider each tuple in base set as a selection
  query.
• e.g. Make Toyota, Model Camry, Price
  10k, Year 2000
• Relax each such query to obtain similar precise
  queries.
• e.g. Make Toyota, Model Camry, Price
  , Year 2000
• Execute and determine tuples having similarity
  above some threshold.
• Challenge Which attribute should be relaxed
  first?
• Make ? Model ? Price ? Year ?
• Solution Relax least important attribute
  first.

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Least Important Attribute

• Definition An attribute whose binding value
  when changed has minimal effect on values binding
  other attributes.
• Does not decide values of other attributes
• Value may depend on other attributes
• E.g. Changing/relaxing Price will usually not
  affect other attributes but changing Model
  usually affects Price
• Dependence between attributes useful to decide
  relative importance
• Approximate Functional Dependencies Approximate
  Keys
• Approximate in the sense that they are obeyed by
  a large percentage (but not all) of tuples in the
  database
• Can use TANE, an algorithm by Huhtala et al
  1999

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Deciding Attribute Importance

• Mine AFDs and Approximate Keys
• Create dependence graph using AFDs
• Strongly connected hence a topological sort not
  possible
• Using Approximate Key with highest support
  partition attributes into
• Deciding set
• Dependent set
• Sort the subsets using dependence and influence
  weights
• Measure attribute importance as

• Attribute relaxation order is all non-keys first
  then keys
• Greedy multi-attribute relaxation

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Tuple Similarity

• Tuples obtained after relaxation are ranked
  according to their
• similarity to the corresponding tuples in base
  set
• where Wi normalized influence weights, ? Wi 1
  , i 1 to Attributes(R)
• Value Similarity
• Euclidean for numerical attributes e.g. Price,
  Year
• Concept Similarity for categorical e.g. Make,
  Model

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Categorical Value Similarity

• Two words are semantically similar if they have a
  common context from NLP
• Context of a value represented as a set of bags
  of co-occurring values called Supertuple
• Value Similarity Estimated as the percentage of
  common Attribute, Value pairs
• Measured as the Jaccard Similarity among
  supertuples representing the values

ST(QMakeToyota)
Model Camry 3, Corolla 4,.
Year 20006,19995 20012,
Price 59954, 65003, 40006
Supertuple for Concept MakeToyota
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Value Similarity Graph
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Empirical Evaluation

• Goal
• Evaluate the effectiveness of the query
  relaxation and similarity estimation
• Database
• Used car database CarDB based on Yahoo Autos
• CarDB( Make, Model, Year, Price, Mileage,
  Location, Color)
• Populated using 100k tuples from Yahoo Autos
• Census Database from UCI Machine Learning
  Repository
• Populated using 45k tuples
• Algorithms
• AIMQ
• RandomRelax randomly picks attribute to relax
• GuidedRelax uses relaxation order determined
  using approximate keys and AFDs
• ROCK RObust Clustering using linKs (Guha et al,
  ICDE 1999)
• Compute Neighbours and Links between every tuple
• Neighbour tuples similar to each other
• Link Number of common neighbours between two
  tuples
• Cluster tuples having common neighbours

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Efficiency of Relaxation
Guided Relaxation
Random Relaxation
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Accuracy over CarDB

• 14 queries over 100K tuples
• Similarity learned using 25k sample
• Mean Reciprocal Rank (MRR) estimated as
• Overall high MRR shows high relevance of
  suggested answers

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Handling Imprecision Incompleteness

• Incompleteness in data
• Databases are being populated by
• Entry by lay people
• Automated extraction
• E.g. entering an accord without mentioning
  Honda

• Imprecision in queries
• Queries posed by lay users
• Who combine querying and browsing

General Solution Expected Relevance Ranking
Challenge Automated Non-intrusive assessment
of Relevance and Density functions
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Handling Imprecision Incompleteness