Data Integration under the Schema Tuple Query Assumption

1
Data Integration under the Schema Tuple Query
Assumption

• Michael Minock
• The University of Umeå, Sweden

2
Introduction

• Problem
• Queries may be over information that is not (yet)
  covered by the data integration system
• List museums in Vienna or Bratislava holding
  paintings by Klimt or Picasso.
• A purely extensional response misleads
• Solution
• Give available extension, but contextualize with
  intensional descriptions of coverage
• Certain The following are all the museums in
  Vienna that hold paintings of Picasso
• Possible The following museums in Vienna do not
  provide inventory records, so they may have
  paintings by Klimt
• Incomplete There is no information for museums
  in Bratislava.

3
Approach

• LAV (Local as View) architecture
• user queries and data source descriptions
  restricted to schema tuple queries in L (or Q)
• currently sources must contain complete and
  correct views
• broker mediates user query over sources and
  supplies a mixed extensional/intensional response
• Use algebraic properties of L (or Q) to derive
• query plan (using cache)
• logical descriptions of certain, uncertain and
  incomplete sets
• Exploit subsumption properties for
• query simplification
• natural language generation

4
The Schema Tuple Query Languages L (and Q)

• Assumptions
• L Tuple relational queries of the form
• Q
• Properties
• L and Q decidable for satisfiability
• Unlike , Q closed over negation
• May calculate difference and intersection and
  decide containment, equivalence and disjointness
  for queries built using L and Q

5
Example Art museum domain
QUERY List museums in Vienna or Bratislava
holding paintings by Klimt or Picasso.
Artist(id, name, country, DOB,DOD)
Museum (id, name, address, city, country)
Painting (id, title,year, artistId)
HasPainting (museumId, paintingId)
Central European Museums
MAK Inventory
Picasso Locator
Albertina Inventory
6
Example Input Expressions
(m Museum (IN m city ("Vienna" "Bratislava"))
( (y1 y2 y3) (HasPainting y1) (Painting y2)
(Artist y3) ( m id y1 museumId) ( y1
paintingId y2 id) ( y2 artistId y3 id) (IN
y3 name ("Klimt" "Picasso"))))
(h HasPainting ( (y1 y2) (Painting y1)
(Artist y2) ( h paintingId y1 id) (
y1 artistId y2 id) ( y2 name "Picasso"))))
(m Museum (IN m city ("Vienna" "Prague
"Berlin ))))
(h HasPainting ( (y1) (Museum y1) (
h museumId y1 id) ( y1 name "MAK") ( y1
city "Vienna"))))
(h HasPainting ( (y1) (Museum y1) (
h museumId y1 id) ( y1 name Albertina")
( y1 city "Vienna"))))
7
Example Output Expressions
(m Museum ( m city Vienna") ( (y1 y2 y3)
(HasPainting y1) (Painting y2) (Artist y3)
( m id y1 museumId) ( y1 paintingId y2 id)
( y2 artistId y3 id) ( y3 name "Picasso")))
(m Museum ( m city Vienna") (IN m name
(Albertina MAK)) ( (y1 y2 y3)
(HasPainting y1) (Painting y2) (Artist y3) (
m id y1 museumId) ( y1 paintingId y2 id) (
y2 artistId y3 id) ( y3 name "Klimt")))
Certain
(m Museum ( m city Vienna") (NOT_IN m name
(Albertina MAK)) ( (y1 y2 y3)
(HasPainting y1) (Painting y2) (Artist y3) (
m id y1 museumId) ( y1 paintingId y2 id) ( y2
artistId y3 id) ( y3 name "Klimt")))
Uncertain
(m Museum ( m city "Bratislava") ( (y1 y2
y3) (HasPainting y1) (Painting y2) (Artist
y3) ( m id y1 museumId) ( y1 paintingId y2
id) ( y2 artistId y3 id) (IN y3 name
("Klimt" "Picasso"))))
Incomplete
8
Example To Natural Language
QUERY List museums in Vienna or Bratislava
holding paintings by Klimt or Picasso.
Museums in Vienna named Albertina or MAK
that have paintings by Klimt.
Certain
Museums in Vienna that have paintings by Picasso
Museums in Vienna not named Albertina or MAK
that have paintings by Klimt.
Uncertain
Incomplete
Museums in Bratislava that have paintings by
Picasso or Klimt.
9
Pros and cons of L and Q

• Pros
• May represent n-ary relations
• Direct translation to SQL!
• Some negation
• General cyclic queries
• The artists without paintings in a museum in
  the country of their origin.
• Cons
• No projection!
• Certain quantifier prefixes prohibited
• The artists with paintings in all of the museums
  in the country of their origin

10
Next STEP

• STEP 1.0 (Schema Tuple Expression Processor)
• Incomplete and/or incorrect source views
• Real applications

Datasource Descriptions
Phrasal Lexicon
Cache DB
Broker
NLG
Differencing Engine/Simplifier
L2DomainCalculus
SPASS theorem prover