1 Lessons from the TSIMMIS Project Yannis Papakonstantinou Department of Computer Science &...

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Lessons from the TSIMMIS Project

Yannis PapakonstantinouDepartment of Computer Science &

Engineering

University of California, San Diego

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Overview

• TSIMMIS’ goals, technical challenges, and solutions

• Insufficiencies of the TSIMMIS’ framework

• Going forward

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Information Resides on Heterogeneous Information Sources

• different interfaces• different data representations• redundant and conflicting information

WWWTickerTape

PersonaldatabaseDialog

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Goal: System Providing Integrated View of Heterogeneous Data

Integration System

WWW Personaldatabase

• collects and combines information• provides integrated view, uniform user interface

TickerTapeDialog

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The Wrapper and Mediator Architecture

Mediator

WrapperWrapper

Client

business reports

portfolios for each company

stock market prices

TickerTape Dialog

CommonData Model

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The Data Warehousing Approach to Integration

Mediator

WrapperWrapper

Client

TickerTape Dialog

Stored Integrated

View

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The Lazy Integration Approach

Mediator

WrapperWrapper

Client

IBM portfolio

IBM price IBM related reports (in common model)

IBM related reports

TickerTape Dialog

Query Decomposition, Translation and Result Fusion

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Mediator

Client

Wrapper

Wrappers & Mediators from High-Level Specifications

Mediator SpecificationInterpreter

WrapperGenerator

Wrapper

WrapperSpecification

MediatorSpecification

Source Source

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Challenge: Sources Without a Well-Structured Schema

• semistructured– irregular– deeply nested– cross-referenced

• incomplete schema knowledge– autonomous– dynamic

• HTML pages• SGML documents• genome data• chemical structures• bibliographic

information• results of the

integration process

Examples

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Challenge: Different and Limited Source Capabilities

Client

Wrapper(A)

Wrapper(B)

Mediator(U = A + B)

retrieve IBM dataretrieve IBM data

retrieve IBM data

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Mediator has to Adapt to Query Capabilities of Sources

Client

Wrapper(A)

Wrapper(B)

Mediator(U = A + B)

retrieve everything

retrieve IBM data

retrieve IBM data

retrieve IBM data

(A) does notallow selection

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Part B

• Semistructured Data Representation

• Mediator Generation

• Wrapper Generation

• Capabilities-Based Rewriting

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Representation of Semistructured Information using OEM

semanticobject-id

label

Atomic Value

Set Value

structuralobject-id

<http://www/~doe, faculty, {&f1,&l1,&r1}> <&f1, first_name, “John”> <&l1, last_name, “Doe”> <&r1, rank, “professor”>

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Graph Representation of OEM Data

faculty first_name “John” last_name “Doe” rank “professor”

http://www/~doe

<http://www/~doe, faculty, {&f1,&l1,&r1}> <&f1, first_name, “John”> <&l1, last_name, “Doe”> <&r1, rank, “professor”>

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OEM Structures Represent Arbitrary Labeled Graphs

faculty first_name “John” last_name “Doe” rank “professor”

http://www/~doe

faculty name “Mary Smith” project “Air DB” paper

author name “John Doe”

author name “Mary Smith”

title “Thin Air DB”

http://www/~smith

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Overview


• Mediator Generation• Example of mediator specification• Language expressiveness• Implementation and performance



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Merge Information Relating to a Faculty

person name “John Doe” birthday “April 1”

s2faculty name “John Doe” rank “professor” papers ...

s1

faculty name “John Doe” rank “professor” birthday “April 1” papers ...

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Mediator Specification Example


s2

<N faculty {<L V>}> :- <faculty {<name N> <L V>}>@s1<N faculty {<L V>}> :- <person {<name N> <L V>}>@s2

faculty name “John Doe” rank “professor” papers ...

s1


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Mediator Specification Example: Semantics of Rule Bodies



s2



s1

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Mediator Specification Example: Semantics of Rule Heads



s2

“John Doe”faculty name “John Doe” rank “professor” birthday “April 1” papers ...


s1

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Incrementally Add to Semantically Identified Object



s1person name “John Doe” birthday “April 1”

s2


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Irregularities & Incomplete Schema Knowledge

<N faculty {<L V>}> :- <faculty {<name N> <L V>}>@s1faculty name “John Doe” rank “professor” papersfaculty name “Mary Smith” project “Air DB”

s1


s2

faculty name “John Doe” rank “professor” birthday “April 1” papers faculty name “Mary Smith” project “Air DB”

“John Doe”

“Mary Smith”

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Second Rule Attaches More Subobjects to View Objects



s1



s2

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Language Expressiveness

• Information fusion problems solved by MSL– Irregularities– Incomplete knowledge of source structure– Transformation of cross-referenced structures– Inconsistent and redundant data– Use of arbitrary matching criteria

• Theoretical analysis of expressiveness– Consider the relational representation of OEM

graphs. Then MSL is equivalent to “SQL + special form of transitive closure”

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faculty name “John Doe” rank “associate”

Inconsistent and Redundant Information


AND NOT <faculty {<name N> <L V1>}>@s1

person name “John Doe” rank “assistant”

s1 s2

“John Doe”faculty

name “John Doe” rank “associate”

rank “assistant”

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Overview


• Mediator Generation• Example of mediator specification• Language expressiveness• Implementation and performance



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Mediator Specification Interpreter Architecture

Query Rewriter

Cost-Based Optimizer

Datamerge Engine


Query

logical datamergeprogram

plan

Result

Queries toWrappers

Results

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Query Rewriting When Known Origins of Information

• <N faculty {<salary S>}> :-:- <faculty {<name N> <salary S>}>@s1

<N faculty {< rank R >}> :- <person {<name N> <rank

R>}>@s2• <well-paid {<name N> <salary X>}>

:- <N faculty {<salary X> <rank assistant>}> AND X>65000

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Query Rewriter Pushes Conditions to Sources

• <N faculty {<salary S>}> :- :- <faculty {<name N> <salary S>}>@s1 <N faculty {< rank R >}>

:- <person {<name N> <rank R>}>@s2• <well-paid {<name N> <salary X>}> :- <N faculty {<salary

X> <rank assistant>}> AND X>65000• logical datamerge program <well-paid {<name

N> <salary X>}> :- (<faculty {<name N> <salary X>}> AND X>65000)@s1

AND <person {<name N> <rank assistant>}>@s2

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<name N> :- <person {<rank assistant>}>

Passing Bindings & Local Join Plans

Passing Bindings

Local Join

<salary X> :- <faculty {<name $N> <salary X>}> AND X>65000

<name N> :- <person {<rank assistant>}>

<a {<s X> <n N>}>:- <faculty {<name N> <salary X>}> AND X>65000

N

s1 s2

s1 s2

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Query Decomposition When Unknown Origins of Information

<X faculty {<S Y>}> :- <X faculty {<birthday “1/20”> <S Y>}>


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Plan Considers All Possible Sources of birthday

<X faculty {<S Y>}> :- <X faculty {<birthday “1/20”> <S Y>}>


name

s2s1

name

birthday

birthday

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Overview

• Semistructured-Data Representation




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Query Translation in Wrappers

Source

SELECT * FROM personSELECT * FROM personWHERE name=“Smith”

find -allfind -n Smith

Query TranslatorResult

Translator

Wrapper

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Rapid Query Translation Using Templates and Actions

Source

SELECT * FROM personSELECT * FROM personWHERE name=“Smith”

find -allfind -n Smith

TemplateInterpreter

ResultTranslator

SELECT * FROM person {emit “find -all” }SELECT * FROM personWHERE name=$N {emit “find -n $N”}

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Description of Infinite Sets of Supported Queries

• uses recursive nonterminals

• Example:– job description contains word w1 and word w2

and ...– SELECT subset(person) FROM person

WHERE \CJob\CJob : job LIKE $W AND \CJob\CJob : TRUE

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Overview

• Semistructured-Data Representation




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Wrapper Supported Queries

Description

Capabilities-Based Rewriter in Mediator Architecture

Capabilities-Based

Rewriter

QueryRewriter

Cost-BasedOptimizer

DatamergeEngine

logical datamerge program

supportedplans

optimal plan


Wrapper Supported Queries

Description

Query

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Capabilities-Based Rewriter Finds Supported Plans

Supported Queries

SELECT * FROM AWHERE salary>65000

SELECT * FROM A

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Capabilities-Based Rewriter Finds Most-Selective Supported Plans

Supported Queries

SELECT * FROM BWHERE salary>65000

SELECT * FROM BSELECT * FROM BWHERE salary >65000

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Capabilities-Based Rewriter Architecture

Component SubQueryDiscovery

Plan Construction

Plan Refinement

Query CapabilitiesDescription

Component SubQueries

Plans (not fully optimized)

Query

Algebraically optimal plans

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What TSIMMIS Achieved

• system for integration of heterogeneous sources

• challenges and solutions– semistructured data & incomplete schema

knowledge• appropriate specification language and query processing

algorithms

– limited and different query capabilities• query translation algorithm

• capabilities-based query rewriting algorithm

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Overview



• Going forward

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Insufficiencies of the TSIMMIS framework

• OEM was really unstructured data– some loose and partial schematic info may

pay off tremendously

• too “databasy” user/mediator/source interaction

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Overview



• Going forward

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Web emerges as a Distributed DB and XML as its Data Model

DataSource

Native XMLDatabase

XML ViewDocument(s)

XML ViewDocument(s)

XML ViewDocument(s)

Also export:1. Schemas & Metadata (XML-Data, RDF,…)2. Description of supported queries

Wrapper

LegacySource

XMAS QueryLanguage

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Definition of Integrated Views

DataSource

DataSource

DataSource

Mediator

XML ViewDocument(s)

Integrated XML View

XML ViewDocument(s)

XML ViewDocument(s)

View Definition inXMAS

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Non-Materialized Views in the MIX mediator system

Blended Browsing &Querying (BBQ) GUI

Application

DOM for Virtual XML Doc’s

MIX Mediator

XMAS query XML document

DTDInference

IntegratedView DTD

XML Source XML Source

QueryProcessor

View Definition inXMAS

Source DTD

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RDB2XMLWrapper

DTDInference

Resolution

Simplification

Execution

Unfolded Query

Blended Browsing &Querying (BBQ) GUI

MIX MediatorXMAS MediatorView Definition

View DTD

Translation to Algebra

Optimization

XML DocumentFragments

XMAS Query

XMLSource 1

DTD

XMASQuery

XMLDocumentFragments

DOM (VXD) Client API

Application

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