Data Integration Techniques

Zachary G. IvesUniversity of Pennsylvania

CIS 550 – Database & Information Systems

October 30, 2003

Some slide content may be courtesy of Susan Davidson, Dan Suciu, & Raghu Ramakrishnan

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We Left Off with 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

This system, like the Information Manifold, focused on querying web sources Real-world integration companies (IBM, BEA, Actuate,

…) are focusing on the enterprise – more $$$!

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Queries in TSIMMIS

Specified in OQL-style language called Lorel OQL was an object-oriented query language Lorel is a predecessor to XQuery; OEM is a predecessor to

XML

Based on path expressions over OEM structures:select bookwhere book.author = “DB2 UDB” and book.title = “Chamberlin”

This is basically like XQuery, which we’ll use in place of Lorel and the MSL template language. Restating the query above:

for $b in document(“mediated-schema”)/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 These are called input bindings Common for web forms (see Amazon, Google, …) XQuery functions (XQuery’s version of views) support

parameters as well, so we’ll use these to illustrate

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A Wrapper Definition in MSL, Translated to XQuery

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 $x in sql(“select * from book where author=‘” + $x +”’”)

return <book>$x<author>$x</author></book>}

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How to Answer the Query

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

We want to find all wrapper definitions that: Either output enough information that we can evaluate

all of our conditions over the output They return a book’s title, and author so we can test

against these Or have already “enforced” the conditions for us!

They already do a selection on author=“Chamberlin,” etc.

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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 of these

In our example, we find one wrapper definition that matches:define function GetBook($x AS xsd:string) as book* {

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

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

}

for $b in document(“mediated-schema”)/bookwhere $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b

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Matching View Output to Our Query’s Conditions

Determine that the query tests for $x=“Chamberlin” by matching the query’s XPath, $b/author/text(), on the function’s output:

define function GetBook($x AS xsd:string) as book {for $b in

sql(“select * from book where author=‘” + $x +”’”)return <book>$b<author>$x</author></book>

}

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

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The Final Step: Unfolding

The expression:let $x := “Chamberlin”

for $b in { for $b in sql(“select * from book where author=‘” + $x +”’”)

return <book>$b<author>$x</author></book> }/bookwhere $b/title/text() = “DB2 UDB” return $b

Can be unnested (“unfolded”) and simplified to:for $b in sql(“select * from book where author=‘Chamberlin’”)where $b/title/text() = “DB2 UDB” return $b

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What Is the Answer?

Given schema book(author, year, title) and Datalog rules defining an instance:

book(“Chamberlin”, “1992”, “DB2 UDB”)book(“Chamberlin”, “1995”, “DB2/CS”)book(“Bernstein”, “1997”, “Transaction Processing”)

TSIMMIS is an instance of a global-as-view mediator with a semistructured data model

Can also have GAV mediators using Datalog or SQL, which work on similar principles

Queries and mappings are unfolded (macro-expanded + simplified)

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Limitations of Global-As-View

Some data sources may contain data that falls within certain ranges or has certain known properties “Books by Aho”, “Students at UPenn”, … How do we express these? (Important so we

reduce the number of sources we query!)

Mediated schema is basically the union of the various MSL templates – as they change, so may the mediated schema! Not good for scalability or flexibility

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Observations of Levy et al. inInformation Manifold Paper

When you integrate something, you have a conceptual model of the integrated domain Define that as a basic frame of reference – not the data

that’s in the sources

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 Information Manifold

Defines the mediated schema independently of the sources! “Local-as-view” instead of “global-as-view” Assumes that we can only see a small subset of all the

possible facts – “open-world assumption” Allows us to specify information about data sources Focuses on relations (with OO extensions), Datalog

Guarantees soundness of answers, completeness of “certain answers” – those tuples that must exist Maximal set of tuples in query answer that are logically

implied by data at the sources, plus all mappings’ constraints

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The Local-as-View Model

Properties: “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 “Answering queries using views” …

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Answering Queries Using Views

Our assumption for today: conjunctive queries, set semantics Suppose we have a mediated schema:

author(aID, isbn, year), book(isbn, title, publisher) A conjunctive query might be:

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

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

and sources:s1(a,t) author(a, i, _), book(i, t, p), t = “123”s2(a,t) author(a, i, _), book(i, t, p), t = “DB2 UDB”s3(a,t,p) author(a, i, _), book(i, t, p), t = “123”s4(a,i) author(a, i, _), a = “Smith”s5(a,i) author(a, i, _)s6(i,p) book(i, t, p)

We want to compose the query with the source mappings – but they’re 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

s5(a,i) author(a, i, _) then we can also infer that:

author(a, i, ???) s5(a,i)

But how to handle the absence of the 3rd attribute? We know that there must be AT LEAST one instance

of ??? in author for each (a,i) pair 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(a,t) :- author(a, i, _), book(i, t, p)

If we look at the rule:s1(a,t) author(a, i, _), book(i, t, p), t = “123”

Clearly q(a,t) :- s1(a,t) But if apply our inversion procedure, we get:

author(a, NULL, NULL) s1(a,t)book(NULL, t, p) s1(a,t), t = “123”

and there’s no way to figure out how to join author and book on NULL! We need “a special NULL for each a-t combo” so we can

figure out which a’s and t’s go together

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The Solution: “Skolem Functions”

Skolem functions: “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 G’s values)

Skolem functions won’t ever be part of the answer set or the computation They’re just a way of logically generating “special

NULLs”

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Revisiting Our Example

Query: q(a,t) :- author(a, i, _), book(i, t, p)

Mapping rule:s1(a,t) author(a, i, _), book(i, t, p), t = “123”

Inverse rules:author(a, f(a,t), NULL) s1(a,t)

book(f(a,t), t, p) s1(a,t), t = “123”

We can now expand the query: q(a,t) :- author(a, i, NULL), book(i, t, p), i = f(a,t) q(a,t) :- s1(a,t), s1(a,t), t = “123”, i = f(a,t)

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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)

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Levy et al. Alternative Approach: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 bucket’s subgoal: Create mapping between q’s vars and the view’s

var at the same position If satisfiable with substitutions, add to bucket

Do cross-product of buckets, see if result is contained in the query (recall we saw an algorithm to do that)

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Source Capabilities

The simplest form is to annotate the attributes of a relation: Bookbff(auth,title,pub)

But many data integration efforts had more sophisticated models Can a data source support joins between its

relations? Can a data source be sent a relation that it should

join with? In the end, we need to perform parts of the

query in the mediator, and other parts at the sources

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Contributions of the Info Manifold

More robust way of defining mediated schemas and sources Mediated schema is clearly defined, less likely to change Sources can be more accurately described

Relatively efficient algorithms for query reformulation, creating executable plans

Still requires standardization on a single schema Can be hard to get consensus

Some other aspects were captured in related papers Overlap between sources; coverage of data at sources Semi-automated creation of mappings Semi-automated construction of wrappers

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Later Integration Systems Focused on Better Performance

Tukwila/Piazza [Ives+99,Halevy+02] – Washington Descendants of the Information Manifold Similar capabilities, but with adaptive

processing of XML as it is read across streams

Niagara [DeWitt+99] – Wisconsin XML querying of web sources Giving answers a screenful at a time

TelegraphCQ [Chandrasekaran+03] – Berkeley Adaptive, select-project-join queries over

infinite streams