Data Integration

Zachary G. IvesUniversity of Pennsylvania

CIS 550 – Database & Information Systems

April 21, 2023

LSD Slides courtesy AnHai Doan

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A Problem

We’ve 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

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Building a Data Integration System

Create a middleware “mediator” or “data integration system” over the sources Can be warehoused (a data warehouse) or virtual Presents a uniform query interface and schema Abstracts away multitude of sources; consults them for

relevant data Unifies different source data formats (and possibly schemas) Sources are generally autonomous, not designed to be

integrated Sources may be local DBs or remote web sources/services Sources may require certain input to return output (e.g.,

web forms): “binding patterns” describe these

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Data Integration System / Mediator

Typical Data Integration Components

Mediated Schema

Wrapper Wrapper Wrapper

SourceRelations

Mappingsin Catalog

SourceCatalog

Query Results

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Typical Data Integration Architecture

Reformulator

QueryProcessor

SourceCatalog

Wrapper Wrapper Wrapper

Query

Query over sources

SourceDescrs.

Queries +bindings Data in mediated format

Results

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Challenges of Mapping Schemas

In a perfect world, it would be easy to match up items from one schema with another Every table would have a similar table in the other schema Every attribute would have an identical attribute in the other

schema Every value would clearly map to a value in the other schema

Real world: as with human languages, things don’t map clearly! May have different numbers of tables – different

decompositions Metadata in one relation may be data in another Values may not exactly correspond It may be unclear whether a value is the same

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Different Aspects to Mapping

Schema matching / ontology alignmentHow do we find correspondences between attributes?

Entity matching / deduplication / record linking / etc.

How do we know when two records refer to the same thing?

Mapping definition How do we specify the constraints or

transformations that let us reason about when to create an entry in one schema, given an entry in another schema?Let’s see one influential approach to schema matching…

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Standard Schema Matcher Architecture(Established by LSD System)

Suppose user wants to integrate 100 data sources1. User:

manually creates mappings for a few sources, say 3 shows schema matcher these mappings

2. Schema matcher learns from the mappings “Multi-strategy” learning incorporates many types of

info in a general way Knowledge of constraints further helps

3. Matcher proposes mappings for remaining 97 sources

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listed-price $250,000 $110,000 ...

address price agent-phone description

Example

location Miami, FL Boston, MA ...

phone(305) 729 0831(617) 253 1429 ...

commentsFantastic houseGreat location ...

realestate.com

location listed-price phone comments

Schema of realestate.com

If “fantastic” & “great”

occur frequently in data values =>

description

Learned hypotheses

price $550,000 $320,000 ...

contact-phone(278) 345 7215(617) 335 2315 ...

extra-infoBeautiful yardGreat beach ...

homes.com

If “phone” occurs in the name =>

agent-phone

Mediated schema

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Learning from Multiple SourcesUse a set of base matchers

Each exploits well certain types of information: Name learner looks at words in the attribute names Naïve Bayes learner looks at patterns in the data values Etc.

Match schema elements of a new source Apply the base learners

Each returns a score For different attributes one learner is more useful than

another Combine their predictions using a combiner / meta-

learner

Combiner / meta-learner Uses training sources to measure base learner accuracy Weighs each learner based on its accuracy

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<location> Boston, MA </> <listed-price> $110,000</> <phone> (617) 253 1429</> <comments> Great location </>

<location> Miami, FL </> <listed-price> $250,000</> <phone> (305) 729 0831</> <comments> Fantastic house </>

Training the Learners

Naive Bayes Learner

(location, address)(listed-price, price)(phone, agent-phone)(comments, description) ...

(“Miami, FL”, address)(“$ 250,000”, price)(“(305) 729 0831”, agent-phone)(“Fantastic house”, description) ...

realestate.com

Name Learner

address price agent-phone description

Schema of realestate.com

Mediated schema

location listed-price phone comments

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<extra-info>Beautiful yard</><extra-info>Great beach</><extra-info>Close to Seattle</>

<day-phone>(278) 345 7215</><day-phone>(617) 335 2315</><day-phone>(512) 427 1115</>

<area>Seattle, WA</><area>Kent, WA</><area>Austin, TX</>

Applying the Learners

Name LearnerNaive Bayes

Meta-Learner

(address,0.8), (description,0.2)(address,0.6), (description,0.4)(address,0.7), (description,0.3)

(address,0.6), (description,0.4)

Meta-LearnerName LearnerNaive Bayes

(address,0.7), (description,0.3)

(agent-phone,0.9), (description,0.1)

address price agent-phone description

Schema of homes.com Mediated schema

area day-phone extra-info

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Putting It All Together: LSD Schema Matching System

L1 L2 Lk

Mediated schema

Source schemas

Data listings

Training datafor base learners Constraint Handler

Mapping Combination

User Feedback

Domain Constraints

Matching PhaseTraining Phase

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Mappings between Schemas

LSD provides attribute correspondences, but not complete mappings

Many similar systems: COMA, COMA++, Falcon-AO, …Mappings generally are posed as views: define relations

in one schema (typically either the mediated schema or the source schema), given data in the other schema This allows us to “restructure” or “recompose + decompose”

our data in a new way

We can also define mappings between values in a view We use an intermediate table defining correspondences – a

“concordance table” It can be filled in using some type of code, and corrected by

hand

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A Few Mapping Examples

Movie(Title, Year, Director, Editor, Star1, Star2)

Movie(Title, Year, Director, Editor, Star1, Star2)

PieceOfArt(ID, Artist, Subject, Title, TypeOfArt)

MotionPicture(ID, Title, Year)Participant(ID, Name, Role)

CustID

CustName

1234 Smith, J.

PennID

EmpName

46732 John Smith

PieceOfArt(I, A, S, T, “Movie”) :- Movie(T, Y, A, _, S1, S2),ID = T || Y, S = S1 || S2

Movie(T, Y, D, E, S1, S2) :- MotionPicture(I, T, Y), Participant(I, D, “Dir”), Participant(I, E, “Editor”), Participant(I, S1, “Star1”), Participant(I, S2, “Star2”)

T1 T2

Need a concordance table from CustIDs to PennIDs

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Two Important Approaches

TSIMMIS [Garcia-Molina+97] – Stanford Focus: semistructured data (OEM), OQL-based language

(Lorel) Creates a mediated schema as a view over the sources Spawned a UCSD project called MIX, which led to a

company now owned by BEA Systems Other important systems of this vein: Kleisli/K2 @ Penn

Information Manifold [Levy+96] – AT&T Research Focus: local-as-view mappings, relational model Sources defined as views over mediated schema

Requires a special Led to peer-to-peer integration approaches (Piazza, etc.)

Focus: Web-based queriable sources

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TSIMMIS

One of the first systems to support semi-structured data, which predated XML by several years: “OEM”

An instance of a “global-as-view” mediation system We define our global schema as views over the

sources

We’ll use XQuery + XML to illustrate the principles

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Some Simple Data<book> <author>Bernstein</author> <author>Newcomer</author> <title>Principles of TP</title></book>

<book> <author>Chamberlin</author> <title>DB2 UDB</title></book>

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Queries in TSIMMIS

Specified in OQL-style language called Lorel OQL was an object-oriented query language that looks

like SQL Lorel is, in many ways, a predecessor to XQuery

Example in XQuery:for $b in AllData()/bookwhere $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b

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Query Answering in TSIMMIS

Basically, it’s 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 they’ll return answers Common for web forms (see Amazon, Google, …) XQuery functions (XQuery’s version of views) support

parameters as well, so we’ll see these in action

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A Wrapper Definition in MSL

Wrappers have templates and binding patterns ($X) in MSL:

B :- B: <book {<author $X>}> // $$ = “select * from book where author=“ $X //

This reformats a SQL query over Book(author, year, title)

In XQuery, this might look like:define function GetBook($x AS xsd:string) as book {

for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x

+”’”)return <book>{$b/title}<author>$x</author></book>

}

book

title author

… …

…

The union of GetBook’s results is unioned with others to form the view Mediator()

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How to Answer the Query

Given our query:for $b in Mediator()/bookwhere $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b

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 xsd:string) as book {for $b in

sql(“Amazon.DB”, “select * from book where author=‘” + $x + “’”)

return <book> {$b/title} <author>{$x}</author></book>}for $b in Mediator()/book

where $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b

book

title author

… …

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Matching View Output to Our Query’s Conditions

Determine that $b/book/author/text() $x by matching the pattern on the function’s output:define function GetBook($x AS xsd:string) as book {

for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x +

“’”)return <book>{ $b/title } <author>{$x}</author></book>

}

let $x := “Chamberlin”for $b in GetBook($x)/bookwhere $b/title/text() = “DB2 UDB” return $b

book

title author

… …

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The Final Step: Unfolding

let $x := “Chamberlin”for $b in (

for $b’ in sql(“Amazon.com”,

“select * from book where author=‘” + $x + “’”) return <book>{ $b/title }<author>{$x}</author></book> )/bookwhere $b/title/text() = “DB2 UDB” return $b

How do we simplify further to get to here?for $b in sql(“Amazon.com”,

“select * from book where author=‘Chamberlin’”)where $b/title/text() = “DB2 UDB” return $b

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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, doesn’t 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, don’t

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 Approach:The 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”

May have overlapping/incomplete sources Define each source as the subset of a query over

the mediated schema 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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Query Answering

Assumption: conjunctive queries, set semanticsSuppose we have a mediated schema:

author(aID, isbn, year), book(isbn, title, publisher)Suppose we have the query:

q(a, t) :- author(a, i, _), book(i, t, p), t = “DB2 UDB”

and sources:s1(a,t) author(a, i, _), book(i, t, p), t = “123”…s5(a, t, p) author(a, i, _), book(i,t), p = “SAMS”

We want to compose the query with the source mappings – but they’re in the wrong direction!

Yet: everything in s1, s5 is an answer to the query!

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Answering Queries Using Views

Numerous recently-developed algorithms for these Inverse rules [Duschka et al.]

Bucket algorithm [Levy et al.]

MiniCon [Pottinger & Halevy]

Also related: “chase and backchase” [Popa, Tannen, Deutsch]

Requires conjunctive queries

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Summary of Data Integration

Local-as-view integration has replaced global-as-view as the standard More robust way of defining mediated schemas and sources Mediated schema is clearly defined, less likely to change Sources can be more accurately described

Methods exist for query reformulation, including inverse rules

Integration requires standardization on a single schema Can be hard to get consensus Today we have peer-to-peer data integration, e.g., Piazza

[Halevy et al.], Orchestra [Ives et al.], Hyperion [Miller et al.]

Data integration capabilities in commercial products: Oracle Fusion, IBM’s WebSphere Integrator, numerous packages from middleware companies