from semistructured data to xml dan suciu at&t labs suciu/vldb99-tutorial.pdf

From Semistructured Data to XML

Dan Suciu

AT&T Labshttp://www.research.att.com/~suciu/vldb99-tutorial.pdf

How the Web is Today

• HTML documents

• all intended for human consumption

• many generated automatically by applications

Easy to fetch any Web page, from any server, any platform

Limits of the Web Today

• application cannot consume HTML

• HTML wrapper technology is brittle– screen scraping

• OO technology (Corba) requires controlled environment

• companies merge, form partnerships; need interoperability fast

people are inventive: send data by fax !

Paradigm Shift on the Web

• new Web standard XML:– XML generated by applications– XML consumed by applications

• data exchange– across platforms: enterprise interoperability– across enterprises

Web: from collection of documents to data and documents

Database Community Can Help

• query optimization, processing

• views, transformations

• data warehouses, data integration

• mediators, query rewriting

• secondary storage, indexes

But Needs a Paradigm Shift Too

• Web data differs from database data:– self-describing, schema-less– structure changes without notice– heterogeneous, deeply nested, irregular– documents and data mixed together

• designed by document, not db experts

• need Web data management

What This Tutorial is About

• what the database community has done– semistructured data model– query languages, schemas

• what the Web community has done:– data formats/models: XML, RDF– transformation language (XSL), schemas

• where they meet and where they differ

Outline

• Semistructured data and XML

• Query languages

• Schemas

• Systems issues

• Conclusions

Part 1Semistructured Data and XML

Semistructured Data

Origins:

• integration of heterogeneous sources

• data sources with non-rigid structure

• biological data

• Web data

The Semistructured Data Model

&o12 &o24 &o29

&o43&96

&243 &206

“Serge”“Abiteboul”

“Victor”“Vianu”

122 133

paperbook

references

referencesreferences

authortitle

yearhttp

author

authorauthor

title publisherauthor

authortitle

firstnamelastname

firstname lastname firstlast

Object Exchange Model (OEM)

complex object

atomic object

Syntax for Semistructured Data

Bib: &o1 { paper: &o12 { … },

book: &o24 { … },

paper: &o29

{ author: &o52 “Abiteboul”,

author: &o96 { firstname: &243 “Victor”,

lastname: &o206 “Vianu”},

title: &o93 “Regular path queries with constraints”,

references: &o12,

references: &o24,

pages: &o25 { first: &o64 122, last: &o92 133}

Syntax for Semistructured Data

May omit oid’s:

{ paper: { author: “Abiteboul”,

author: { firstname: “Victor”,

lastname: “Vianu”},

title: “Regular path queries …”,

page: { first: 122, last: 133 }

Characteristics of Semistructured Data

• missing or additional attributes

• multiple attributes

• different types in different objects

• heterogeneous collections

self-describing, irregular data, no a priori structure

Comparison with Relational Data

{ row: { name: “John”, phone: 3634 },

row: { name: “Sue”, phone: 6343 },

row: { name: “Dick”, phone: 6363 }

n a m e p h o n e

J o h n 3 6 3 4

S u e 6 3 4 3

D i c k 6 3 6 3

row row row

name name namephone phone phone

“John” 3634“Sue” “Dick”6343 6363

• a W3C standard to complement HTML

• origins: structured text SGML

• motivation:– HTML describes presentation– XML describes content

• http://www.w3.org/TR/REC-xml (2/98)

SGMLXMLHTML4.0

From HTML to XML

HTML describes the presentation

<h1> Bibliography </h1>

<p> <i> Foundations of Databases </i>

Abiteboul, Hull, Vianu

<br> Addison Wesley, 1995

Abiteoul, Buneman, Suciu

<br> Morgan Kaufmann, 1999

XML<bibliography>

<book> <title> Foundations… </title>

<author> Abiteboul </author>

<author> Vianu </author>

<publisher> Addison Wesley </publisher>

</book>

</bibliography>XML describes the content

XML Terminology• tags: book, title, author, …• start tag: <book>, end tag: </book>• elements: <book>…<book>,<author>…</author>• elements are nested• empty element: <red></red> abbrv. <red/>• an XML document: single root element

well formed XML document: if it has matching tags

More XML: Attributes

<title> Foundations of Databases </title>

<author> Abiteboul </author>

</book>

attributes are alternative ways to represent data

More XML: Oids and References

</person>

</person>

oids and references in XML are just syntax

XML Data Model

• does not exists• Document Object Model (DOM):

– http://www.w3.org/TR/REC-DOM-Level-1 (10/98)– class hierarchy (node, element, attribute,…)– objects have behavior– defines API to inspect/modify the document

XML Parsers

• traditional: return data structure (DOM?)

• event based: SAX (Simple API for XML)– http://www.megginson.com/SAX– write handler for start tag and for end tag

XML Namespaces

• http://www.w3.org/TR/REC-xml-names (1/99)• name ::= [prefix:]localpart

<isbn:number> …. </isbn:number>

</book>

XML Namespaces

• syntactic: <number> , <isbn:number>

• semantic: provide URL for schema

<mystyle:title> … </mystyle:title>

<mystyle:number> …

</tag>

defined here

XML v.s. Semistructured Data

• both described best by a graph

• both are schema-less, self-describing

Similarities and Differences

</person>

{ person: &o123

{ name: “Alan”,

age: 42,

email: “ab@com” }

person

name age email

Alan 42 ab@com

person

name age email

Alan 42 ab@com

father father

{ person: { father: &o123 …}}

similar on trees, different on graphs

More Differences

• XML is ordered, ssd is not

• XML can mix text and elements: <talk> Making Java easier to type and easier to type

<speaker> Phil Wadler </speaker>

</talk>

• XML has lots of other stuff: entities, processing instructions, comments

• http://www.w3.org/TR/REC-rdf-syntax (2/99)

• purpose: metadata for Web– help search engines

• syntax in XML

• semantics: edge-labeled graphs

RDF Syntax

<rdf:Description about=“www.mypage.com”>

<about> birds, butterflies, snakes </about>

<lastname> Smith </lastname>

</rdf:Description>

</author>

</rdf:Description>

RDF Data Model

www.mypage.com

birds, butterflies, snakes

John Smith

about author

firstname lastname

the RDF Data Model is very close to semistructured data

More RDF Examples

www.mypage.com

birds, butterflies, snakes

John Smith

about author

firstname lastname

www.anotherpage.com

author

<rdf:Description about=“www.mypage.com”>

<about> birds, butterflies, snakes </about>

<lastname> Smith </lastname>

</rdf:Description> </author>

</rdf:Description>

<rdf:Description about=“www.anotherpage.com”> <related> <rdf:Description about=“www.mypage.com”/> </related> <author rdf:resource=“&o55”/> <author> Joe Doe </author></rdf:Description>

RDF Terminology

subject

object

predicate

statement

O E M R D Fn o d e r e s o u r c el a b e l p r o p e r t y

s o u r c e / l a b e l / d e s t s u b j e c t / p r e d i c a t e / o b j e c te d g e s t a t e m e n t

More RDF: Containers• bag, sequence, alternative

<rdf:Description> <a> <rdf:Bag>

<rdf:li> s1 </rdf:li>

<rdf:li> s2 </rdf:li>

</rdf:Bag>

</rdf:Description>

RDF Containers (cont’d)

Bag s1 s2

rdf:typerdf_1

More RDF: Higher Order Statements

“the author of www.thispage.com says: ‘the topic of www.thatpage.com is environment’ “

www.thatpage.com

environment

www.thispage.com

author

RDF uses reification

Summary of Data Models

• semistructured data, XML, RDF

• data is self-describing, irregular

• schema embedded in the data

Part 2Query Languages

• Query languages

• Schemas

• Systems issues

• Conclusions

Query Languages: Motivation

• granularity of the HTML Web: one file

• granularity of Web data varies:– single data item: “get John’s salary”– entire database: “get all salaries”– aggregates: “get average salary”

• need query language to define granularity

Query Languages: Outline

• for semistructured data:– Lorel– UnQL– StruQL

• for XML: XML-QL• a different paradigm

– structural recursion– XSL

• part of the Lore system (Stanford)

• adapts OQL to semistructured data

select X.titlefrom Bib.paper Xwhere X.year > 1995

select Bib.paper.titlefrom Bib.paperwhere Bib.paper.year > 1995

example:

abbreviated to:

Lorel v.s. OQL

• implicit coercions: 1995 to “1995”

• missing attributes– empty answer v.s. type error

• set-valued attributes– in X.year>1995, X may have several years

• regular path expressions (next)

Regular Path Expressions

Useful for:• syntactic substitute for inheritance: paper|book• navigating partially known structures: lastname?• transitive closure: reference+

select X.titlefrom Bib.paper X, Bib.(paper|book) Ywhere Y.author.lastname? = “Ullman” and Y.reference+ X

select Twhere Bib.paper: { title: T, year: Y, journal: “TODS”} and Y > 1995

• Unstructured Query Language

• patterns, templates, structural recursion

• patterns:

UnQL: Templates

select result: { fn: F, ln: L, pub: { title: T, year: Y }}where Bib.paper: { title: T, year: Y, journal: “TODS”} and Y > 1995

Result looks like: { result: { fn: “John”, ln: “Smith”, pub: { title: “P equals NP”, year: 2005}}, result: { fn: “Joe”, ln: “Doe”, pub: { title: “Errata to P=NP”, year: 2006}} … }

Skolem Functions

• Maier, 1986– in OO systems

• Kifer et al, 1989– F-logic

• Hull and Yoshikawa, 1990– deductive db (ILOG)

• Papakonstantinou et al., 1996– semistructured db (MSL)

• illustrate with Strudel (next)

Skolem Functions in StruQL

• Strudel: a Web Site Management System

• StruQL: its query language

Example: Bibliography Data

{Bib: { paper: { author: “Jones”,

author: “Smith”,

title: “The Comma”,

year: 1994

{ paper: ….. }

Example: A Complex Web Site

Root()

YearPage(“Smith”,1994)

YearPage(“Smith”,1996)

YearPage(“Jones”,1994)

YearPage(“Jones”,1998)

YearPage(“Mark”,1996)

yearentry yearentry yearentryyearentry yearentry

publication

publicationPubPage(“The Comma”) PubPage(“The Dot”)

publication publicationpublication

title title

author

HomePage(“Smith”) HomePage(“Jones”) HomePage(“Mark”)

personperson

person

Example: Skolem Functions in

StruQLwhere Root -> “Bib” -> X, X -> “paper” -> P, P -> “author” -> A, P -> “title” -> T, P -> “year” -> Y

create Root(), HomePage(A), YearPage(A,Y), PubPage(P)

link Root() -> “person” -> HomePage(A), HomePage(A) -> “yearentry” -> YearPage(A,Y), YearPage(A,Y) -> “publication” -> PubPage(P), PubPage(P) -> “author” -> HomePage(A), PubPage(P) -> “title” -> T

where Root -> “Bib” -> X, X -> “paper” -> P, P -> “author” -> A, P -> “title” -> T, P -> “year” -> Y

create Root(), HomePage(A), YearPage(A,Y), PubPage(P)

link Root() -> “person” -> HomePage(A), HomePage(A) -> “yearentry” -> YearPage(A,Y), YearPage(A,Y) -> “publication” -> PubPage(P), PubPage(P) -> “author” -> HomePage(A), PubPage(P) -> “title” -> T

XML-QL: A Query Language for XML

• http://www.w3.org/TR/NOTE-xml-ql (8/98)

• features:– regular path expressions– patterns, templates– Skolem Functions

• based on OEM data model

Pattern Matching in XML-QL

where <book language=“french”> <publisher> <name> Morgan Kaufmann </name> </publisher> <author> $a </author> </book> in “www.a.b.c/bib.xml”construct $a

Simple Constructors in XML-QL

Note: </> abbreviates </book> or </result> or ...

where <book language = $l> <author> $a </> </> in “www.a.b.c/bib.xml”construct <result> <author> $a </> <lang> $l </> </>

<result> <author>Smith</author><lang>English</lang></result><result> <author>Smith</author><lang>Mandarin</lang></result><result> <author>Doe</author><lang>English</lang></result>

Skolem Functions in XML-QL

where <book language = $l> <author> $a </> </> in “www.a.b.c/bib.xml”construct <result> <author id=F($a)> $a</> <lang> $l </> </>

<result> <author>Smith</author> <lang>English</lang> <lang>Mandarin</lang> </result><result> <author>Doe</author> <lang>English</lang> </result>

A Different Paradigm: Structural Recursion

Data as sets with a union operator:

{a:3, a:{b:”one”, c:5}, b:4} =

{a:3} U {a:{b:”one”,c:5}} U {b:4}

Structural Recursion

f(T1 U T2) = f(T1) U f(T2)f({L: T}) = f(T)f({}) = {}f(V) = if isInt(V) then {result: V} else {}

Example: retrieve all integers in the data

“one” 5

4result result result

standard textbook programming on trees

Structural Recursion

Example: increase all engine prices by 10%

f(T1 U T2) = f(T1) U f(T2)f({L: T}) = if L= engine then {L: g(T)} else {L: f(T)}f({}) = {}f(V) = V

g(T1 U T2) = g(T1) U g(T2)g({L: T}) = if L= price then {L:1.1*T} else {L: g(T)}g({}) = {}g(V) = V

engine body

part price

price price

part price

engine body

part price

price price

part price

• two W3C drafts: XSLT and XPATH– http://www.w3.org/TR/xpath, 7/99– http://www.w3.org/TR/WD-xslt, 7/99

• in commercial products (e.g. IE5.0)

• purpose: stylesheet specification language:– stylesheet: XML -> HTML– in general: XML -> XML

XSL Templates and Rules

• query = collection of template rules

• template rule = match pattern + template

<xsl:template> <xsl:apply-templates/> </xsl:template>

<xsl:template match = “/bib/*/title”> <result> <xsl:value-of/> </result></xsl:template>

Retrieve all book titles:

XPath Expressions in Match Patterns

bib matches a bib element

* matches any element

/ matches the root element

/bib matches a bib element under root

bib/paper matches a paper in bib

bib//paper matches a paper in bib, at any depth

//paper matches a paper at any depth

paper|book matches a paper or a book

@price matches a price attribute

bib/book/@price matches price attribute in book, in bib

Flow Control in XSL

<xsl:template match=“a”> <A><xsl:apply-templates/></A></xsl:template>

<xsl:template match=“b”> <B><xsl:apply-templates/></B></xsl:template>

<xsl:template match=“c”> <C><xsl:value-of/></C></xsl:template>

<xsl:template match=“a”> <A><xsl:apply-templates/></A></xsl:template>

<xsl:template match=“b”> <B><xsl:apply-templates/></B></xsl:template>

<xsl:template match=“c”> <C><xsl:value-of/></C></xsl:template>

XSL is Structural Recursion

Equivalent to:

f(T1 U T2) = f(T1) U f(T2)f({L: T}) = if L= c then {C: t} else L= b then {B: f(t)} else L= a then {A: f(t)} else f(t)f({}) = {}f(V) = V

XSL query = single functionXSL query with modes = multiple function

XSL and Structural Recursion

XSL:• trees only• may loop

Structural Recursion:• arbitrary graphs• always terminates

<xsl:template match = “e”> <xsl:apply-patterns select=“/”/></xsl:template>

stack overflow on IE 5.0

add the following rule:

Summary of Query Languages

• studied extensively in semistructured data

• some quite powerful features

• no standard for XML QL yet (WG soon)

• XSL available today (for stylesheets)

• XSL = structural recursion

Part 3Schemas

• Query languages

• Schemas

• Systems issues

• Conclusions

Schemas

• why ?– XML: to describe semantics– semistructured data: to improve processing

• what ?– semistructured data: foundational – XML: several concrete proposals

here lies our interest

Schemas

• when ?– semistructured data, XML: a posteriori– RDBMS: a priori, to interpret binary data

• how ?– semistructured data: schema is independent – XML: schema is hardwired with the data

Outline

• schemas for semistructured data:– foundations– schema extraction

• schemas for XML:– DTD– XML-Schema– RDF-Schema

Schemas: An Example

&c1 &c2

&s2 &s3 &s6 &s7

companycompany

nameaddress name

address

“Widget” “Trenton” “Gadget”

“www.gp.fr”

“Paris”

&p2&p1 &p3

&s0 &s1 &s4 &s5 &s8 &s9

personperson

person

“Smith”

nameposition name phonename

position

“Manager” “Jones” “5552121” “Dupont” “Sales”

employeemanages

c.e.o.works-for works-for

works-for

c.e.o.

&a2 &a3

&a4&a5

&a6&a7

description

procurement salesrep

contact

eval1997

“on target”

“below target”

Some database:

Lower-Bound Schemas

Company Employee

string

companyperson

works-for

c.e.o.

address

managed-by

Upper Bound Schemas

Company Employee

string

companyperson

works-for

c.e.o. | employee

name | address | url

managed-by

name | phone | position

description

The Two Questions to Ask

Conformance: does that data conform to this schema ?

Classification: if so, then which objects belong to what classes ?

Graph Simulation

Definition Two edge-labeled graphs G1, G2

A simulation is a relation R between nodes:• if (x1, x2) in R, and (x1,a,y1) in G1,

then exists (x2,a,y2) in G2 (same label)

s.t. (y1,y2) in R

Note: a simulation can be efficiently computed [Henzinger, et a. 1995]

Using Simulation

Data graph D, schema S

• upper bound schema:– conformance: find simulation R from D to S– classification: check if (x,c) in R

• lower bound schema– conformance: find simulation R from S to D– classification: check if (c,x) in R

[Buneman et al 1997]

Example

&c1 &c2

&s2 &s3 &s6 &s7

companycompany

nameaddress name

address

“Widget” “Trenton” “Gadget”

“www.gp.fr”

“Paris”

&p2&p1 &p3

&s0 &s1 &s4 &s5 &s8 &s9

personperson

person

“Smith”

nameposition namephonename

position

“Manager” “Jones” “5552121” “Dupont” “Sales”

employeemanages

c.e.o.works-for works-for

works-forc.e.o.

&a2 &a3

&a4&a5

&a6&a7

description

procurement salesrep

contact

eval1997

“on target”

“below target”

Company Employee

string

company

person

works-for

c.e.o.

address

managed-by

Company Employee

string

company

person

works-for

c.e.o. | employee

name | address | url

managed-by

name | phone | position

description

DatabaseLower Bound Upper Bound

simulation: efficient technique for checking conformance to schema

Application 1: Improve Secondary Storage

Company Employee

string

company

person

works-for

c.e.o.

address

managed-by

o i d n a m e a d d r e s s c . e . o .… … … …… … … …

Company

o i d n a m e m a n a g e d - b y w o r k s - f o r… … … …… … … …

Employee

Store rest in overflow graph

Lower-bound schema

Application 2: Query Optimization

paper book

yearjournal

int string string

addressauthor

zip city street

lastname

firstname

string string string string string

string

select X.titlefrom Bib._ Xwhere X.*.zip = “12345”

select X.titlefrom Bib.book Xwhere X.address.zip = “12345”

Upper-bound schema[Fernandez, Suciu 1998]

Schema Extraction(From Data)

Problem statement

• given data instance D

• find the “most specific” schema S for D

In practice: S too large, need to relax

[Nestorov et al. 1998]

Schema Extraction: Sample Data

&p8&p1 &p2 &p3 &p4 &p5 &p6 &p7

company

employeeemployee

employeeemployee employee employee

employeeemployee

worksfor

worksforworksforworksfor

worksforworksfor

worksfor

manages

managedby

managedbymanagedby

manages

managedby

Lower Bound Schema Extraction

Root&r

Bosses&p1,&p4,&p6

Regulars&p2,&p3,&p5,&p7,&p8

Company&c

company employee

manages

managedby

worksfor

employee

Upper Bound Schema Extraction: Data Guides

Root&r

Employees&p1,&p1,&p3,P4

&p5,&p6,&p7,&p8

Bosses&p1,&p4,&p6

Regulars&p2,&p3,&p5,&p7,&p8

Company&c

company

employee

managesmanagedby

manages

managedby

worksfor

Schemas in XML

• Document Type Definition (DTD)

• XML Schema

• RDF Schema

Document Type Definition: DTD

• part of the original XML specification

• an XML document may have a DTD

• terminology for XML:– well-formed: if tags are correctly closed– valid: if it has a DTD and conforms to it

• validation is useful in data exchange

DTDs as Grammars

<!DOCTYPE paper [ <!ELEMENT paper (section*)> <!ELEMENT section ((title,section*) | text)> <!ELEMENT title (#PCDATA)> <!ELEMENT text (#PCDATA)>]>

DTDs as Schemas

Not so well suited:• impose unwanted constraints on order

<!ELEMENT person (name,phone)>

• references cannot be constraint

• can be to vague: <!ELEMENT person ((name|phone|email)*)>

XML Schemas

• very recent proposal

• unifies previous schema proposals

• generalizes DTDs

• uses XML syntax

• two documents: structure and datatypes– http://www.w3.org/TR/xmlschema-1– http://www.w3.org/TR/xmlschema-2

XML Schemas<elementType name=“paper”>

</choice>

</sequence>

</elementType>DTD: <!ELEMENT paper (title,author*,year, (journal|conference))>

RDF Schemas

• http://www.w3.org/TR/PR-rdf-schema (3/99)

• object-oriented flavor

RDF Schemas

• recall RDF data:– resources– properties

• RDF schema:– classes– properties

subject

object

predicate

statement

RDF Schemas

<rdf:Description ID=“car001”>

<name> My Honda </name>

<rdf:type resource=“#MotorVehicle”/>

</rdf:Description>

RDF SchemasSchema:

<rdf:Description ID=“MotorVehicle”>

<rdf:type resource=“#Class”/>

<rdf:subClassOf resource=“#Resource”/>

</rdf:Description>

<rdf:Description ID=“Truck”>

<rdf:type resource=“#Class”/>

<rdf:subClassOf resource=“#MotorVehicle”/>

</rdf:Description>

RDF Schemas

MotorVehicle

car001

Classtype

subClassOf

name miles

My Honda 50000

RDF Schemas

• different from object-oriented systems:– OO: define a class by set of properties– RDF: define a property in terms of its classes

• metadata in RDF:– an RDF schema described as an RDF data

Summary of Schemas

• in SS data: – graph theoretic– data and schema are decoupled– used in data processing

• in XML– from grammar to object-oriented– schema wired with the data– emphasis on semantics for exchange

Part 4Systems Issues

• Query languages

• Schemas

• Systems issues

• Conclusions

Systems Issues

• servers

• mediators

Servers for Semistructured Data / XML

• storage

• index• query evaluation [McHugh, Widom 1999]

XML Storage

• text file (XML)

• store in ternary relation

• use DTD to derive schema

• mine data to derive schema

• build special purpose repository (Lore)

XML Storage: Text File

• advantages– simple– less space than one thinks– reasonable clustering

• disadvantage– no updates– require special purpose query processor

&o4 &o5

title author authoryear

“The Calculus” “…” “…” “1986”

Store XML in Ternary Relation

[Florescu, Kossman 1999]

S o u r c e L a b e l D e s t

& o 1 p a p e r & o 2& o 2 t i t l e & o 3& o 2 a u t h o r & o 4& o 2 a u t h o r & o 5& o 2 y e a r & o 6

N o d e V a l u e

& o 3 T h e C a l c u l u s& o 4 …& o 5 …& o 6 1 9 8 6

Use DTD to derive Schema

• DTD:

• ODMG classes:

• [Christophides et al. 1994 , Shanmugasundaram et al. 1999]

<!ELEMENT employee (name, address, project*)><!ELEMENT address (street, city, state, zip)>

class Employee public type tuple (name:string, address:Address, project:List(Project))class Address public type tuple (street:string, …)

Mine Data to Derive Schema

paperpaper paper

authorauthor author author author

titletitle title title

fn fn fn fn lnlnlnln

a u t h o r t i t l eX X

f n 1 l n 1 f n 2 l n 2 t i t l e y e a r

X X X X X -X X - - X XX X - - X -

Paper1

Paper2

[Deutsch et al. 1999]

Indexing Semistructured Data

• coercions: 1995 v.s. “1995”

• regular path expressions– data guides [Goldman, Widom, 1997]– T-indexes [Milo, Suciu, 1999]

Indexing All Paths in the Data1

2 3 4 5 6

7 8 9 10 11 12 13

t t t t t

a b a c a d a a b

Semistructured Data

2 3 4 5 6

7 8 10 12 13 7 13 9 11

Data Guide

2 3 4 5 6

7 13 8 10 12 9 11

ab c db

T-Index

Mediators for Semistructured Data / XML

• XML = virtual view of Relational/OO/OR sources• mediator = translation, integration• issues:

– query composition and rewriting [Papakonstatinou, et al. 1996]– limited source capabilities [Yerneni, et al. 1999]

Example: An XML Mediator

• relational database:

• virtual XML view:

s i d n a m e… …… …

Stores i d b i d… …… …

SBb i d t i t l e… …… …

• specify mediator declaratively (a view):

from Store, SB, Bookwhere Store.sid=SB.sid and SB.bid=Book.bidconstruct <store ID=f(Store.sid)> <name> Store.name </name> <book> Book.title </book> </store>

• users ask XML-QL queries:– find stores who sell “The Calculus”

where <store> <name> $n </name> <book> The Calculus </book> <store>construct <result> $n </result>

• system composes query with view:

from Store, SB, Bookwhere Store.sid=SB.sid and SB.bid=Book.bid and Book.title=“The Calculus”construct <result> Store.name </result>

Summary of Systems

• unclear today how XML will be used– materialized ? Need servers– virtual ? Need mediators

• most work is still ahead

Part 5Conclusions

• Query languages

• Schemas

• Systems issues

• Conclusions

Summary

• XML = what is out there

• semistructured data = what we can process

• paradigm shift, for both Web and db

• covered in tutorial:– data models, queries, schemas

Current and Future Technologies

• Web applications possible today:– export relational data to XML (e.g. Oracle)– import XML directly into applications

• Web applications in the future:– mediator technology (XML view)– store/process native XML data– compress XML– mine/analyze XML

Why This Is Cool for Database Researchers

• put to work what you teach in CS101 !– tree traversals (structural recursion, XSL)– automata theory (DTD’s, path expressions)– graph theory (simulation)

• adapt old DB tricks to new kind of data

• save the trees: from fax to XML

The End

from semistructured data to xml dan suciu at&t labs suciu/vldb99-tutorial.pdf

database data

data formatsmodels

irregular data

relational data

data integration mediators

data modelquery languages

paradigm shift tooweb

web community

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