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Schema Mapping as Query Discovery. Renee J. Miller Laura M. Haas Mauricio A. Hernandez Presented by: Helen Chen. Introduction. Modern applications need schema mappings Current schema mapping process is done manually In Clio , schema mapping = query discovery

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schema mapping as query discovery

Schema Mapping as Query Discovery

Renee J. Miller

Laura M. Haas

Mauricio A. Hernandez

Presented by: Helen Chen

introduction
Introduction
  • Modern applications need schema mappings
  • Current schema mapping process is done manually
  • In Clio, schema mapping = query discovery
    • Modern DBMS manage not only data but also queries
introduction cont
Introduction (cont’)
  • Schema mappings cannot be fully automated
  • Outside sources are needed
  • Clio is a prototype tool for semi-automated schema mapping/query discovering
characteristics of clio
Characteristics of Clio
  • Clio is VC driven
  • VCs are an appropriate abstraction for eliciting information from the user or DBA
  • Using reasoning about queries and query containment can help the user derive correct schema mappings
principle in mapping construction
Principle in Mapping Construction
  • All possible values in source  target
    • Use union rather than join
  • A value from the source  target
    • Use join rather than cross product
  • Override the principles is permitted

once

search space
Search Space
  • Vertical compositions (join)
      • Requires to consider mappings between schemas with constraints and dependencies
  • Horizontal compositions (set operators)
      • Source and target schemas do not represent the same information
query discovery notation
Query Discovery Notation
  • Let S1, … Snrepresent the n source relation
  • Let T1, … Tmrepresent the m target relation
  • Use symbol A to denote source attributes
    • The domain of an attribute A is denoted dom(A)
    • The meta-data associated with A is denoted (A)
  • Use symbol B to denote target attributes
query discovery notation cont
Query Discovery Notation (cont’)
  • Value correspondence i = <fi, pi>
    • A function (fi)
      • q >=1
      • fi: dom(A1) x … dom(Aq) x m(A1) x … m(Aq) dom(B)
    • A filter (pi)
      • pi: dom(A1) x … dom(Ar) x m(A1) x … m(Ar) boolean
core query discovery algorithm

Potential

Sets P

Candidate Sets G

All fi

A Cover 

All source relations

All pi

Core Query Discovery Algorithm
example
Example
  • Consider the following value correspondences
    • f1: S1.A  T.C
    • f2: S2.A  T.D
    • f3: S2.B  T.C
    • All three filters are True
example cont
Example (cont’)
  • P = {{1, 2},{2, 3},{1},{2},{3}}
  • G = {{1, 2},{2, 3},{1},{2},{3}}
  • Cover

1 = {{1, 2},{2, 3}}

2 = {{1},{2, 3}}

  • SQL Query
another example

q2: SELECT P.HrRate*W.Hrs

FROM PayRate P, WorksOn W,

Student S

WHERE P.Rank = W.ProjRank

AND

S.Yr = P.Rank

q1: SELECT P.HrRate*W.Hrs

FROM PayRate P, WorksOn W

WHERE P.Rank = W.ProjRank

Another Example

f1: PayRate(HrRate)*WorkdOn(Hrs) Personnel(Sal)

another example cont
Another Example (cont’)

f2: Professor(Sal) Personnel(Sal)

p2: True

f1: PayRate(HrRate)*WorkdOn(Hrs) Personnel(Sal)

p1: True

q3: SELECT P.HrRate*W.Hrs

FROM PayRate P, WorksOn W,

Student S

WHERE P.Rank = W.ProjRank

AND

S.Yr = P.Rank

UNION ALL

SELECT Sal

FROM Professor

 = {{1}, {2}}

incremental query discovery algorithm

Add/Delete a Value

Correspondence

u

i



i+1

Incremental Query Discovery Algorithm

u’

SQL

Query

conclusion
Conclusion
  • Schema mapping construction process is searching for the most reasonable mapping
  • Clio uses VCs to help users create schema mappings
  • Clio can produce both flat and nested relational targets
  • VC framework can be extended to both GAV and LAV
limitation
Limitation
  • VCs are entered by user of linguistic techniques – semi-automated