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From Semistructured Data to XML Dan Suciu AT&T Labs http://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 1 Semistructured Data and XML Semistructured Data Origins: • integration of heterogeneous sources • data sources with non-rigid structure • biological data • Web data The Semistructured Data Model Bib &o1 complex object paper paper book references &o12 &o24 references author title year &o29 references author http page author title publisher title author author author &o43 &25 &96 1997 last firstname lastname atomic object firstname lastname &243 “Serge” “Abiteboul” “Victor” Object Exchange Model (OEM) first &206 “Vianu” 122 133 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 nam e phone John 3634 Sue 6343 D ic k 6363 row row name phone name phone name phone “John” 3634 “Sue” 6343 “Dick” 6363 { row: { name: “John”, phone: 3634 }, row: { name: “Sue”, phone: 6343 }, row: { name: “Dick”, phone: 6363 } } XML • a W3C standard to complement HTML • origins: structured text SGML • motivation: – HTML describes presentation – XML describes content • HTML4.0 XML SGML • http://www.w3.org/TR/REC-xml (2/98) From HTML to XML HTML describes the presentation HTML <h1> Bibliography </h1> <p> <i> Foundations of Databases </i> Abiteboul, Hull, Vianu <br> Addison Wesley, 1995 <p> <i> Data on the Web </i> Abiteoul, Buneman, Suciu <br> Morgan Kaufmann, 1999 XML <bibliography> <book> <title> Foundations… </title> <author> Abiteboul </author> <author> Hull </author> <author> Vianu </author> <publisher> Addison Wesley </publisher> <year> 1995 </year> </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 <book price = “55” currency = “USD”> <title> Foundations of Databases </title> <author> Abiteboul </author> … <year> 1995 </year> </book> attributes are alternative ways to represent data More XML: Oids and References <person id=“o555”> <name> Jane </name> </person> <person id=“o456”> <name> Mary </name> <children idref=“o123 o555”/> </person> <person id=“o123” mother=“o456”><name>John</name> </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 <book xmlns:isbn=“www.isbn-org.org/def”> <title> … </title> <number> 15 </number> <isbn:number> …. </isbn:number> </book> XML Namespaces • syntactic: <number> , <isbn:number> • semantic: provide URL for schema <tag xmlns:mystyle = “http://…”> defined here … <mystyle:title> … </mystyle:title> <mystyle:number> … </tag> XML v.s. Semistructured Data • both described best by a graph • both are schema-less, self-describing Similarities and Differences <person id=“o123”> { person: &o123 <name> Alan </name> { name: “Alan”, <age> 42 </age> age: 42, <email> ab@com </email> email: “ab@com” } </person> } <person father=“o123”> … </person> father person { person: { father: &o123 …} } person father name age email name Alan age 42 email ab@com Alan similar on trees, different on graphs 42 ab@com 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 RDF • 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> <author> <rdf:Description> <firstname> John </firstname> <lastname> Smith </lastname> </rdf:Description> </author> </rdf:Description> RDF Data Model www.mypage.com about author birds, butterflies, snakes firstname John lastname Smith the RDF Data Model is very close to semistructured data More RDF Examples related www.mypage.com about www.anotherpage.com author author birds, butterflies, snakes author Joe Doe firstname John lastname Smith <rdf:Description about=“www.mypage.com”> <about> birds, butterflies, snakes </about> <author> <rdf:Description ID=“&o55”> <firstname> John </firstname> <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 predicate object statement OEM node la b e l s o u rc e /la b e l/d e s t edge RDF re so u rc e p ro p e rty s u b je c t/p re d ic a te /o b je c t s ta te 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> </a> </rdf:Description> RDF Containers (cont’d) a rdf:type rdf_1 Bag s1 rdf_2 s2 More RDF: Higher Order Statements “the author of www.thispage.com says: ‘the topic of www.thatpage.com is environment’ “ www.thispage.com www.thatpage.com topic author says environment RDF uses reification Summary of Data Models • semistructured data, XML, RDF • data is self-describing, irregular • schema embedded in the data Part 2 Query Languages • • • • • Semistructured data and XML 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 Lorel • part of the Lore system (Stanford) • adapts OQL to semistructured data example: select X.title from Bib.paper X where X.year > 1995 select Bib.paper.title abbreviated to: from Bib.paper where Bib.paper.year > 1995 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 select X.title from Bib.paper X, Bib.(paper|book) Y where Y.author.lastname? = “Ullman” and Y.reference+ X Useful for: • syntactic substitute for inheritance: paper|book • navigating partially known structures: lastname? • transitive closure: reference+ UnQL • Unstructured Query Language • patterns, templates, structural recursion • patterns: select T where Bib.paper: { title: T, year: Y, journal: “TODS”} and Y > 1995 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 person Root() person person HomePage(“Smith”) yearentry HomePage(“Jones”) yearentry yearentry author YearPage(“Smith”, 1994) yearentry publication PubPage(“The Comma”) title yearentry YearPage(“Jones”, 1994) YearPage(“Smith”, 1996) author HomePage(“Mark”) YearPage(“Mark”, 1996) YearPage(“Jones”, 1998) publication publication PubPage(“The Dot”) publication title publication author Example: Skolem Functions in StruQL 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 where <book language = $l> <author> $a </> </> in “www.a.b.c/bib.xml” construct <result> <author> $a </> <lang> $l </> </> Note: </> abbreviates </book> or </result> or ... <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 Example: retrieve all integers in the data f(T1 U T2) = f({L: T}) = f({}) = f(V) = a 3 f(T1) U f(T2) f(T) {} if isInt(V) then {result: V} else {} b a result b c “one” 5 4 3 result 5 result 4 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 engine part price 100 body price part 1000 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 price price 1000 100 part price 110 body price part 1100 price price 1000 100 XSL • 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 Retrieve all book titles: <xsl:template> <xsl:apply-templates/> </xsl:template> <xsl:template match = “/bib/*/title”> <result> <xsl:value-of/> </result> </xsl:template> XPath Expressions in Match Patterns bib * / /bib bib/paper bib//paper //paper paper|book @price bib/book/@price matches a bib element matches any element matches the root element matches a bib element under root matches a paper in bib matches a paper in bib, at any depth matches a paper at any depth matches a paper or a book matches a price attribute matches price attribute in book, in bib Flow Control in XSL <xsl:template> <xsl:apply-templates/> </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> <a> <e> <b> <c> 1 </c> <c> 2 </c> </b> <a> <c> 3 </c> </a> </e> <c> 4 </c> </a> <A> <B> <C> 1 </C> <C> 2 </C> </B> <A> <C> 3 </C> </A> <C> 4 </C> </A> 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 function XSL query with modes = multiple function XSL and Structural Recursion XSL: • trees only • may loop Structural Recursion: • arbitrary graphs • always terminates add the following rule: <xsl:template match = “e”> <xsl:apply-patterns select=“/”/> </xsl:template> stack overflow on IE 5.0 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 3 Schemas • • • • • Semistructured data and XML Query languages Schemas Systems issues Conclusions Schemas • why ? here lies our interest – XML: to describe semantics – semistructured data: to improve processing • what ? – semistructured data: foundational – XML: several concrete proposals 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 Some database: &r1 person person company person manages company works-for employee &p1 &c1 &p2 &c2 c.e.o.&p3 c.e.o. works-for position phone name address namepositionworks-for name name nameaddress &s0 &s1 &s2 &s3 &s4 &s5 &s6 url &s7 &s8 &s9 description “Smith” “Manager” “Widget” “Trenton” “Jones” description “Sales” &s10 eval &a5 1998 &a4 1997 &a7 &a3 task &a6 “below target” “www.gp.fr” &a1 salesrep procurement “Paris” “Dupont” “5552121” “Gadget” &a2 contact “on target” Lower-Bound Schemas person Root company works-for managed-by Company c.e.o. name address name Employee string Upper Bound Schemas person Root company works-for managed-by Company Employee c.e.o. | employee name | address | url name | phone | position description string - Any 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 x1 G1 R x2 a a y1 R G2 y2 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 person Root company works-for managed-by Company c.e.o. “Smith” “Manager” name address name Employee &r1 person person companyperson manages company works-for &p1 c.e.o. &c1employee&p2 &c2 c.e.o.&p3 works-for works-for phone name address position name position name nameaddress name &s0 &s1 &s2 &s3 &s4 &s5 &s6 url &s7 &s8 &s9description string “Widget” “Trenton” “Jones” &a1 procurement &a2 “Paris”“Dupont” “Sales” “5552121”“Gadget” description salesrep contact works-for managed-by Company Employee c.e.o. | employee name | address | url name | phone | position &s10 eval &a5 1998 &a4 1997 &a7 &a3 task &a6 “below target” “www.gp.fr” person Root company description string - “on target” Lower Bound Database Upper Bound simulation: efficient technique for checking conformance to schema Any Application 1: Improve Secondary Storage Company person Root company works-for managed-by Company nam e … … a d d re ss … … c .e .o . … … name address name Employee c.e.o. o id … … string Lower-bound schema Employee o id … … nam e … … m a n a g e d -b y … … w o rk s-fo r … … Store rest in overflow graph Application 2: Query Optimization Bib paper year int journal select X.title from Bib._ X where X.*.zip = “12345” book address title string string title author string string last first zip city streetname name string string string string Upper-bound schema select X.title from Bib.book X where X.address.zip = “12345” [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 &r employee employee employee employee employee manages employee employee manages manages manages manages &p1 &p2 managedby &p3 company worksfor worksfor &p4 &p5 &p6 &p7 managedby managedby managedby worksfor employee managedby worksfor worksfor worksfor worksfor worksfor &c &p8 Lower Bound Schema Extraction Root &r employee company employee Bosses &p1,&p4,&p6 worksfor Company &c manages managedby worksfor Regulars &p2,&p3,&p5,&p7,&p8 Upper Bound Schema Extraction: Data Guides Root &r company managedby employee Employees &p1,&p1,&p3,P4 &p5,&p6,&p7,&p8 manages worksfor Bosses &p1,&p4,&p6 worksfor Company &c manages managedby worksfor Regulars &p2,&p3,&p5,&p7,&p8 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)> ]> <paper> <section> <text> </text> </section> <section> <title> </title> <section> … </section> <section> … </section> </section> </paper> 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”> <sequence> <elementTypeRef name=“title”/> <elementTypeRef name=“author” minOccurs=“0”/> <elementTypeRef name=“year”/> <choice> <elementTypeRef name=“journal”/> <elementTypeRef name=“conference”/> </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: subject predicate object statement – resources – properties • RDF schema: – classes – properties RDF Schemas Data: <rdf:Description ID=“car001”> <name> My Honda </name> <miles> 50000 </miles> <rdf:type resource=“#MotorVehicle”/> </rdf:Description> RDF Schemas Schema: <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 car001 name type Truck subClassOf type MotorVehicle type Class 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 4 Systems Issues • • • • • Semistructured data and XML 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 Store XML in Ternary Relation Ref S o u rc e &o1 & & & & & paper &o2 title &o3 author author &o4 “The Calculus” “…” year &o5 “…” [Florescu, Kossman 1999] &o6 “1986” o1 o2 o2 o2 o2 Val N ode & & & & o3 o4 o5 o6 L abel D est paper title a u th o r a u th o r year & & & & & o2 o3 o4 o5 o6 V a lu e T h e C a lc u lu s … … 1986 Use DTD to derive Schema • DTD: <!ELEMENT employee (name, address, project*)> <!ELEMENT address (street, city, state, zip)> • ODMG classes: class Employee public type tuple (name:string, address:Address, project:List(Project)) class Address public type tuple (street:string, …) • [Christophides et al. 1994 , Shanmugasundaram et al. 1999] Mine Data to Derive Schema paper paper paper Paper1 paper year author title author authortitle authortitleauthor title fn ln fn ln fn ln fn fn 1 ln 1 fn 2 ln 2 title year X X X X X X X - X - X X X X - ln Paper2 a u th o r X [Deutsch et al. 1999] title X 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 Data 1 t t 2 a t 3 b 7 a 8 t t 4 c a d 5 a 9 10 11 12 6 a b 13 Semistructured Data t 1 t 23456 a 7 8 10 12 13 d c b 7 13 Data Guide a 9 11 7 13 1 23456 b c b 8 10 12 T-Index d 9 11 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 Store • relational database: • virtual XML view: s id … … SB nam e … … s id … … Book b id … … <store> <name> n1 </name> <book> ... </book> <book> ... </book> ... </store> <store> <name>n2 </name> <book> ... </book> <book> ... </book> … </store> b id … … title … … Example: An XML Mediator • specify mediator declaratively (a view): from where Store, SB, Book Store.sid=SB.sid and SB.bid=Book.bid construct <store ID=f(Store.sid)> <name> Store.name </name> <book> Book.title </book> </store> Example: An XML Mediator • 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> Example: An XML Mediator • system composes query with view: from Store, SB, Book where 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 5 Conclusions • • • • • Semistructured data and XML 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 Further Readings www. w3.org/XML www-db.stanford.edu/~widom www-rocq.inria.fr/~abiteboul db.cis.upenn.edu www.research.att.com/~suciu Abiteboul, Buneman, Suciu Data on the Web: From Relational to Semistructured to XML Morgan Kaufmann, 1999 (appears in October)