Data Integration Methods

1
Data Integration Methods

• Zachary G. Ives
• University of Pennsylvania
• CIS 650 Database Information Systems
• February 16, 2004

2
Administrivia

• Next reading assignment on scalable query
  reformulation algorithms
• Pottinger and Halevy MiniCon
• Write-up summarize the main ideas of this paper

3
Todays Trivia Question
4
A Problem

• Weve seen that even with normalization and the
  same needs, different people will arrive at
  different schemas
• In fact, most people also have different needs!
• Often people build databases in isolation, then
  want to share their data
• Different systems within an enterprise
• Different information brokers on the Web
• Scientific collaborators
• Researchers who want to publish their data for
  others to use
• This is the goal of data integration tie
  together different sources, controlled by many
  people, under a common schema

5
Example

• We want to build the UltimateMovieGuideTM
• Given
• TV Guide schema ShowingAt(time, title,
  year) Shows(title, year, genre,
  rating) Director(title, year, name) Starring(tit
  le, year, name)
• GoodMovies.com FourStarMovie(title, year,
  genre) DecentMovie(title, year)
• OscarWinners WinningDirector(director, title,
  year)
• Documentaries.org Documentary(title, year,
  director, producer)

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Integrating Data

• Several steps
• Getting data out from a data source it may have
  its own query/retrieval interface
• Sometimes it will need a query before it returns
  answers, e.g., a Web form
• Getting all of the data into the same data model
  and format
• Translating the data into the same schema
• Answering queries
• How might we handle this?

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Data Warehouses Offline Replication

• Get experts together, define a schema they think
  best captures all the info
• Define a database with this schema
• Define procedural mappings in an ETL tool to
  import the data
• Perhaps perform data cleaning
• Periodically copy all of the data from the data
  sources
• Note that the sources and the warehouse are
  basically independent at this point

Results
Query
Data Warehouse
Remote, Autonomous Data Sources
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Pros and Cons of Data Warehouses

• Need to spend time to design the physical
  database layout, as well as logical
• This actually takes a lot of effort!
• Data is generally not up-to-date (lazy or offline
  refresh)
• Queries over the warehouse dont disrupt the data
  sources
• Can run very heavy-duty computations, including
  data mining and cleaning

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An Alternative Mediators or Virtual Integration
Systems

• Get experts together, define a schema they think
  best captures all the info
• Define as a virtual mediated schema
• Create declarative mappings specifying how to get
  data from each source into the warehouse
• Evaluate queries over the mediated schema on the
  fly using the current data at the sources

Results
Query
Data Integration System
Mediated Schema
Source Catalog
Schema Mappings
Remote, Autonomous Data Sources
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Core Question How Do We Define and Use
Mappings?

• Queries must be directly composed with mappings
• Leads to use of views as the means of specifying
  mappings
• So which direction do we specify views?
• Mediated relations as views over source relations
• Source relations as views over mediated relations
• TSIMMIS chooses option 1
• Information Mainfold chooses option 2
• Neither is perfect or comprehensive, as well see

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The Job of Mappings

• Between different data sources
• May have different numbers of tables different
  decompositions
• Attributes may be broken down differently
  (rating vs. EbertThumb and RoeperThumb)
• Metadata in one relation may be data in another
• Values may not exactly correspond (shows vs.
  movies)
• It may be unclear whether a value is the same
  (COPPOLA vs. Francis Ford Coppola)
• May have different, but synonymous terms (ImdbID
  123456 ? SSN 987-45-3210)
• Might have sub/superclass relationships

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General Techniques

• Value-value correspondences accomplished using
  concordance tables
• Join through a table mapping values to values
• Imdb_Actor(ID, SAG_actor_name)
• Table-multitable correspondences accomplished
  using joins (in one direction), projections (in
  other direction)
• Key question what happens if a needed attribute
  is missing? (e.g., DecentMovie has no genre)
• Super/subclass relationships generally must be
  captured using selection (in one direction),
  union (in other direction)
• And sometimes we just cant specify the
  correspondence!

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Some Examples of Mappings

• Show(ID, Title, Year, Lang, Genre)
• Movie(ID, Title, Year, Genre, Director, Star1,
  Star2)

• EnglishMovie(Title, Year, Genre, Rating)
• Docu(ID, Title, Year)Participant(ID, Name, Role)

PieceOfArt(I, T, Y, English, G) -
EnglishMovie(T, Y, G, _), MovieIDFor(I, T, Y)
Movie(I, T, Y, doc, D, S1, S2) - Docu(I, T,
Y), Participant(I, D, Dir), Participant(I,
S1, Cast1), Participant(I, S2, Cast2)
T1
ImdbID CastOf
1234 Catwoman
Name CastOf
Berry, H. Monsters Ball
T2
Need a concordance table from ImdbIDs to actress
names
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TSIMMIS and Information Manifold

• Focus Web-based queryable sources
• CGI forms, online databases, maybe a few RDBMSs
• Each needs to be mapped into the system not as
  easy as web search but the benefits are
  significant vs. query engines
• A few parenthetical notes
• Part of a slew of works on wrappers, source
  profiling, etc.
• The creation of mappings can be partly automated
  systems such as LSD, Cupid, Clio, do this
• Today most people look at integrating large
  enterprises (thats where the is!) Nimble,
  BEA, IBM

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TSIMMIS

• The Stanford-IBM Manager of Multiple Information
  Sources or, a Yiddish stew
• An instance of a global-as-view mediation
  system
• One of the first systems to support
  semi-structured data, which predated XML by
  several years

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Semi-structured Data OEM

• Observation given a particular schema, its
  attributes may be unavailable from certain
  sources inherent irregularity
• Proposal Object Exchange Model, OEM
• OID ltlabel, type, valuegt

• 1 show 2 id 15 , 3 title Catwoman
  , 4 year 2004 , 5 lang English ,
  6 genre fantasy , 7 criticsrating 8
  stars 0.5 , 9 source Bob

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Queries in TSIMMIS

• Specified in OQL-style language called Lorel
• OQL was an object-oriented query language
• Lorel is, in many ways, a predecessor to XQuery
• Based on path expressions over OEM structures
• select showwhere show.title Star Wars and
  show.genre sci-fi
• This is basically like XQuery, which well use in
  place of Lorel and the MSL template language.
  Previous query restated
• for s in AllData()/showwhere s/title/text()
  Star Wars and s/genre/text()
  sci-fireturn s

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Query Answering in TSIMMIS

• Basically, its view unfolding, i.e., composing a
  query with a view
• The query is the one being asked
• The views are the MSL templates for the wrappers
• Some of the views may actually require
  parameters, e.g., an author name, before theyll
  return answers
• Common for web forms (see Amazon, Google, )
• XQuery functions (XQuerys version of views)
  support parameters as well, so well see these in
  action

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A Wrapper Definition in MSL

• Wrappers have templates and binding patterns (T)
  in MSL
• S - S ltshow ltgenre Ggtgt // select
  from movie where title T //
• This reformats a SQL query over Movie(title,
  year, genre)
• In XQuery, this might look like
• define function GetShow(t AS xsdstring) as show
  for s in sql(Amazon.DB, select
  from movie where title t )return
  ltshowgtlttitlegttlt/titlegt s/year,
  s/genrelt/showgt

movie
genre
year



The union of GetShows results is unioned with
others to form the view AllData()
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How to Answer the Query

• Given our query
• for s in AllData()/showwhere s/title/text()
  Star Wars and s/genre/text()
  sci-fireturn s
• Find all wrapper definitions that
• Contain output enough structure to match the
  conditions of the query
• Or have already tested the conditions for us!

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Query Composition with Views

• We find all views that define book with author
  and title, and we compose the query with each
• define function GetBook(x AS xsdstring) as book
  for b in sql(Amazon.DB, select
  from book where author x )return
  ltbookgt b/title ltauthorgtxlt/authorgtlt/bookgt
• for b in AllData()/bookwhere b/title/text()
  DB2 UDB and b/author/text()
  Chamberlinreturn b

book
author
title


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Example on Board
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Virtues of TSIMMIS

• Early adopter of semistructured data, greatly
  predating XML
• Can support data from many different kinds of
  sources
• Obviously, doesnt fully solve heterogeneity
  problem
• Presents a mediated schema that is the union of
  multiple views
• Query answering based on view unfolding
• Easily composed in a hierarchy of mediators

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Limitations of TSIMMIS Approach

• Some data sources may contain data with certain
  ranges or properties
• Books by Aho, Students at UPenn,
• If we ask a query for students at Columbia, dont
  want to bother querying students at Penn
• How do we express these?
• Mediated schema is basically the union of the
  various MSL templates as they change, so may
  the mediated schema

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An Alternate ApproachThe Information Manifold
(Levy et al.)

• When you integrate something, you have some
  conceptual model of the integrated domain
• Define that as a basic frame of reference,
  everything else as a view over it
• Local as View using mappings that are
  conjunctive queries
• May have overlapping/incomplete sources
• Define each source as the subset of a query over
  the mediated schema the open world assumption
• We can use selection or join predicates to
  specify that a source contains a range of values
• ComputerBooks() ? Books(Title, , Subj), Subj
  Computers

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The Local-as-View Model

• The basic model is the following
• Local sources are views over the mediated
  schema
• Sources have the data mediated schema is
  virtual
• Sources may not have all the data from the domain
  open-world assumption
• The system must use the sources (views) to answer
  queries over the mediated schema

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Answering Queries Using Views

• Assumption conjunctive queries, set semantics
• Suppose we have a mediated schema show(ID,
  title, year, genre), rating(ID, stars, source)
• A conjunctive query might be q(t) - show(i,
  t, y, g), rating(i, 5, s), y 1997
• Recall intuitions about this class of queries
• Adding a conjunct to a query removes answers from
  the result but never adds any
• Any conjunctive query with at least the same
  constraints conjuncts will give valid answers

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Query Answering

• Suppose we have the query
• q(t) - show(i, t, y, g), rating(i, 5, s), y
  1997
• and sources
• 5star(i) ? show(i, t, y, g), rating(i, 5, s)
• TVguide(t,y,g,r) ? show(i, t, y, g), rating(i, r,
  TVGuide)
• movieInfo(i,t,y,g) ? show(i, t, y, g)
• critics(i,r, s) ? rating(i, r, s)
• goodMovies(t,y) ? show(i, t, y, drama),
  rating(i, 5, s), y 1997
• We want to compose the query with the source
  mappings but theyre in the wrong direction!

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Inverse Rules

• We can take every mapping and invert it, though
  sometimes we may have insufficient information
• If
• 5star(i) ? show(i, t, y, g), rating(i, 5, s)
• then we can also infer that
• show(i,??? ,??? ,??? ,???) ? 5star(i)
• But how to handle the absence of the missing
  attributes?
• We know that there must be AT LEAST one instance
  of ??? for each attribute for each show ID
• So we might simply insert a NULL and define that
  NULL means unknown (as opposed to missing)

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But NULLs Lose Information

• Suppose we take these rules and ask for
• q(t) - show(i, t, y, g), rating(i, 5, s), y
  1997
• If we look at the rule
• goodMovies(t,y) ? show(i, t, y, drama),
  rating(i, 5, s), y 1997
• By inspection, q(t) ? goodMovies(t,y)
• But if apply our inversion procedure, we get
• show(i, t, y, g) ? goodMovies(t,y), i NULL, g
  drama
• rating(i, r, s) ? goodMovies(t,y), i NULL, r
  5, s NULL
• We need a special NULL so we can figure out
  which IDs and ratings match up

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The Solution Skolem Functions

• Skolem functions
• Conceptual perfect hash functions
• Each function returns a unique, deterministic
  value for each combination of input values
• Every function returns a non-overlapping set of
  values (Skolem function F will never return a
  value that matches any of Skolem function Gs
  values)
• Skolem functions wont ever be part of the answer
  set or the computation it doesnt produce real
  values
• Theyre just a way of logically generating
  special NULLs

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Query Answering Using Inverse Rules

• Invert all rules using the procedures described
• Take the query and the possible rule expansions
  and execute them in a Datalog interpreter
• In the previous query, we expand with all
  combinations of expansions of book and of author
  every possible way of combining and
  cross-correlating info from different sources
• Then we throw away all unsatisfiable rewritings
  (some expansions will be logically inconsistent)
• More efficient, but equivalent, algorithms now
  exist
• Bucket algorithm Levy et al., which we discuss
  next
• MiniCon Pottinger Halevy (next time)
• Also related chase and backchase Popa,
  Tannen, Deutsch

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The Bucket Algorithm

• Given a query Q with relations and predicates
• Create a bucket for each subgoal in Q
• Iterate over each view (source mapping)
• If source includes buckets subgoal
• Create mapping between qs vars and the views
  var at the same position
• If satisfiable with substitutions, add to bucket
• Do cross-product of buckets, see if result is
  contained (exptime, but queries are probably
  relatively small)
• For each result, do a containment check to make
  sure the rewriting is contained within the query

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Lets Try a Bucket Example

• Query
• q(t) - show(i, t, y, g), rating(i, 5, s), y
  1997
• Sources
• 5star(i) ? show(i, t, y, g), rating(i, 5, s)
• TVguide(t,y,g,r) ? show(i, t, y, g), rating(i, r,
  TVGuide)
• movieInfo(i,t,y,g) ? show(i, t, y, g)
• critics(i,r, s) ? rating(i, r, s)
• goodMovies(t,y) ? show(i, t, y, drama),
  rating(i, 5, s), y 1997
• good98(t,y) ? show(i, t, y, drama), rating(i,
  5, s), y 1998

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Example of Containment Testing

• Suppose we have two queriesq1(S,C) -
  Student(S, N), Takes(S, C), Course(C, X),
  inCSE(C), Course(C, DB Info
  Systems)q2(S,C) - Student(S, N), Takes(S, C),
  Course(C, X)
• Intuitively, q1 must contain the same or fewer
  answers vs. q2
• It has all of the same conditions, except one
  extra conjunction (i.e., its more restricted)
• Theres no union or any other way it can add more
  data
• We can say that q2 contains q1 because this holds
  for any instance of our DB Student, Takes,
  Course

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Checking Containment via Canonical Databases

• To test for q1 µ q2
• Create a canonical DB that contains a tuple for
  each subgoal in q1
• Execute q2 over it
• If q2 returns a tuple that matches the head of
  q1, then q1 µ q2
• (This is an NP-complete algorithm in the size of
  the query. Testing for full first-order logic
  queries is undecidable!!!)
• Lets see this for our example

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Example Canonical DB

• q1(S,C) - Student(S, N), Takes(S, C), Course(C,
  X), inCSE(C), Course(C, DB Info Systems)
• q2(S,C) - Student(S, N), Takes(S, C), Course(C,
  X)

Student
Takes
Course
inCSE
C X
C DB Info Systems
S
S N
S C
Need to get tuple ltS,Cgt in executing q2 over this
database
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Next Time

• Well look at the state-of-the-art in query
  reformulation, the MiniCon algorithm
• Eliminates the need for the containment check
• Eliminates many cross-product comparisons
• This and the ChaseBackchase strategy of Tannen
  et al are the two methods most used in virtual
  data integration today
• Please read the MiniCon paper (Pottinger Halevy)