web data management with rdf

Web Data Management in RDF Age

M. Tamer Ozsu

University of WaterlooDavid R. Cheriton School of Computer Science

1PKU/2014-08-28

AcknowledgementsThis presentation draws upon collaborative research anddiscussions with the following colleagues (in alphabetical order)

Gunes Aluc, University of Waterloo

Khuzaima Daudjee, University of Waterloo

Olaf Hartig, University of Waterloo

Lei Chen, Hong Kong University of Science & Technology

Lei Zou, Peking University

2PKU/2014-08-28

Web Data Management

A long term research interest in the DB community

2000 2004

2011 20113PKU/2014-08-28

Interest Due to Properties of Web Data

Lack of a schema

Data is at best “semi-structured”Missing data, additional attributes, similar data but notidentical

Volatility

Changes frequentlyMay conform to one schema now, but not later

Scale

Does it make sense to talk about a schema for Web?How do you capture “everything”?

Querying difficulty

What is the user language?What are the primitives?Arent search engines or metasearch engines sufficient?

4PKU/2014-08-28

More Recent Approaches to Web Querying

Fusion TablesUsers contribute data in spreadsheet, CVS, KML formatPossible joins between multiple data setsExtensive visualization

8PKU/2014-08-28


Fusion TablesUsers contribute data in spreadsheet, CVS, KML formatPossible joins between multiple data setsExtensive visualization

XMLData exchange languagePrimarily tree based structure

<list title="MOVIES">

<film>

<title>The Shining</title>

<director>Stanley Kubrick</director>

<actor>Jack Nicholson</actor>

</film>

<film>

<title>Spartacus</title>

<director>Stanley Kubrick</director>

</film>

<film>

<title>The Passenger</title>

<actor>Jack Nicholson</actor>

</film>

...

</list>

root

film

title

“The Shining”

director

“Stanley Kubrick”

actor

“Jack Nicholson”

film

...

film

title

“The Passenger”

actor


8PKU/2014-08-28


Fusion Tables

Users contribute data in spreadsheet, CVS, KML formatPossible joins between multiple data setsExtensive visualization

XML

Data exchange languagePrimarily tree based structure

RDF (Resource Description Framework) & SPARQL

W3C recommendationSimple, self-descriptive modelBuilding block of semantic web & Linked Open Data (LOD)

8PKU/2014-08-28

RDF and Semantic Web

RDF is a language for the conceptual modeling of informationabout resources (web resources in our context)

A building block of semantic web

Facilitates exchange of informationSearch engine results can be more focused and structuredFacilitates data integration (mashes)

Machine understandable

Understand the information on the web and theinterrelationships among them

9PKU/2014-08-28

RDF Uses

Yago and DBpedia extract facts from Wikipedia & representas RDF → structural queries

Communities build RDF data

E.g., biologists: Bio2RDF and Uniprot RDF

Web data integration

Linked Open Data Cloud

. . .

10PKU/2014-08-28

RDF Data Volumes . . .

. . . are growing – and fast

Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year

11PKU/2014-08-28




As of March 2009

LinkedCTReactome

Taxonomy

KEGG

PubMed

GeneID

Pfam

UniProt

OMIM

PDB

SymbolChEBI

Daily Med

Disea-some

CAS

HGNC

InterPro

Drug Bank

UniParc

UniRef

ProDom

PROSITE

Gene Ontology

HomoloGene

PubChem

MGI

UniSTS

GEOSpecies

Jamendo

BBCProgramm

es

Music-brainz

Magna-tune

BBCLater +TOTP

SurgeRadio

MySpaceWrapper

Audio-Scrobbler

LinkedMDB

BBCJohnPeel

BBCPlaycount

Data

Gov-Track

US Census Data

riese

Geo-names

lingvoj

World Fact-book

Euro-stat

IRIT Toulouse

SWConference

Corpus

RDF Book Mashup

Project Guten-berg

DBLPHannover

DBLPBerlin

LAAS- CNRS

Buda-pestBME

IEEE

IBM

Resex

Pisa

New-castle

RAE 2001

CiteSeer

ACM

DBLP RKB

Explorer

eprints

LIBRIS

SemanticWeb.org Eurécom

ECS South-ampton

RevyuSIOCSites

Doap-space

Flickrexporter

FOAFprofiles

flickrwrappr

CrunchBase

Sem-Web-

Central

Open-Guides

Wiki-company

QDOS

Pub Guide

Open Calais

RDF ohloh

W3CWordNet

OpenCyc

UMBEL

Yago

DBpediaFreebase

Virtuoso Sponger

March ’09:89 datasets

11PKU/2014-08-28

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.http://lod-cloud.net/




As of September 2010

MusicBrainz

(zitgist)

P20

YAGO

World Fact-book (FUB)

WordNet (W3C)

WordNet(VUA)

VIVO UFVIVO

Indiana

VIVO Cornell

VIAF

URIBurner

Sussex Reading

Lists

Plymouth Reading

Lists

UMBEL

UK Post-codes

legislation.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov

.uk

totl.net

Tele-graphis

TCMGeneDIT

TaxonConcept

The Open Library (Talis)

t4gm

Surge Radio

STW

RAMEAU SH

statisticsdata.gov

.uk

St. Andrews Resource

Lists

ECS South-ampton EPrints

Semantic CrunchBase

semanticweb.org

SemanticXBRL

SWDog Food

rdfabout US SEC

Wiki

UN/LOCODE

Ulm

ECS (RKB

Explorer)

Roma

RISKS

RESEX

RAE2001

Pisa

OS

OAI

NSF

New-castle

LAAS

KISTIJISC

IRIT

IEEE

IBM

Eurécom

ERA

ePrints

dotAC

DEPLOY

DBLP (RKB

Explorer)

Course-ware

CORDIS

CiteSeer

Budapest

ACM

riese

Revyu

researchdata.gov

.uk

referencedata.gov

.uk

Recht-spraak.

nl

RDFohloh

Last.FM (rdfize)

RDF Book

Mashup

PSH

ProductDB

PBAC

Poké-pédia

Ord-nance Survey

Openly Local

The Open Library

OpenCyc

OpenCalais

OpenEI

New York

Times

NTU Resource

Lists

NDL subjects

MARC Codes List

Man-chesterReading

Lists

Lotico

The London Gazette

LOIUS

lobidResources

lobidOrgani-sations

LinkedMDB

LinkedLCCN

LinkedGeoData

LinkedCT

Linked Open

Numbers

lingvoj

LIBRIS

Lexvo

LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

Good-win

Family

Jamendo

iServe

NSZL Catalog

GovTrack

GESIS

GeoSpecies

GeoNames

GeoLinkedData(es)

GTAA

STITCHSIDER

Project Guten-berg (FUB)

MediCare

Euro-stat

(FUB)

DrugBank

Disea-some

DBLP (FU

Berlin)

DailyMed

Freebase

flickr wrappr

Fishes of Texas

FanHubz

Event-Media

EUTC Produc-

tions

Eurostat

EUNIS

ESD stan-dards

Popula-tion (En-AKTing)

NHS (EnAKTing)

Mortality (En-

AKTing)Energy

(En-AKTing)

CO2(En-

AKTing)

educationdata.gov

.uk

ECS South-ampton

Gem. Norm-datei

datadcs

MySpace(DBTune)

MusicBrainz

(DBTune)

Magna-tune

John Peel(DB

Tune)

classical(DB

Tune)

Audio-scrobbler (DBTune)

Last.fmArtists

(DBTune)

DBTropes

dbpedia lite

DBpedia

Pokedex

Airports

NASA (Data Incu-bator)

MusicBrainz(Data

Incubator)

Moseley Folk

Discogs(Data In-cubator)

Climbing

Linked Data for Intervals

Cornetto

Chronic-ling

America

Chem2Bio2RDF

biz.data.

gov.uk

UniSTS

UniRef

UniPath-way

UniParc

Taxo-nomy

UniProt

SGD

Reactome

PubMed

PubChem

PRO-SITE

ProDom

Pfam PDB

OMIM

OBO

MGI

KEGG Reaction

KEGG Pathway

KEGG Glycan

KEGG Enzyme

KEGG Drug

KEGG Cpd

InterPro

HomoloGene

HGNC

Gene Ontology

GeneID

GenBank

ChEBI

CAS

Affy-metrix

BibBaseBBC

Wildlife Finder

BBC Program

mesBBC

Music

rdfaboutUS Census

Media

Geographic

Publications

Government

Cross-domain

Life sciences

User-generated content

September ’10:203 datasets

11PKU/2014-08-28





As of September 2011

MusicBrainz

(zitgist)

P20

Turismo de

Zaragoza

yovisto

Yahoo! Geo

Planet

YAGO

World Fact-book

El ViajeroTourism

WordNet (W3C)

WordNet (VUA)

VIVO UF

VIVO Indiana

VIVO Cornell

VIAF

URIBurner

Sussex Reading

Lists

Plymouth Reading

Lists

UniRef

UniProt

UMBEL

UK Post-codes

legislationdata.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov.

uk

Traffic Scotland

theses.fr

Thesau-rus W

totl.net

Tele-graphis

TCMGeneDIT

TaxonConcept

Open Library (Talis)

tags2con delicious

t4gminfo

Swedish Open

Cultural Heritage

Surge Radio

Sudoc

STW

RAMEAU SH

statisticsdata.gov.

uk

St. Andrews Resource

Lists

ECS South-ampton EPrints

SSW Thesaur

us

SmartLink

Slideshare2RDF

semanticweb.org

SemanticTweet

Semantic XBRL

SWDog Food

Source Code Ecosystem Linked Data

US SEC (rdfabout)

Sears

Scotland Geo-

graphy

ScotlandPupils &Exams

Scholaro-meter

WordNet (RKB

Explorer)

Wiki

UN/LOCODE

Ulm

ECS (RKB

Explorer)

Roma

RISKS

RESEX

RAE2001

Pisa

OS

OAI

NSF

New-castle

LAASKISTI

JISC

IRIT

IEEE

IBM

Eurécom

ERA

ePrints dotAC

DEPLOY

DBLP (RKB

Explorer)

Crime Reports

UK

Course-ware

CORDIS (RKB

Explorer)CiteSeer

Budapest

ACM

riese

Revyu

researchdata.gov.

ukRen. Energy Genera-

tors

referencedata.gov.

uk

Recht-spraak.

nl

RDFohloh

Last.FM (rdfize)

RDF Book

Mashup

Rådata nå!

PSH

Product Types

Ontology

ProductDB

PBAC

Poké-pédia

patentsdata.go

v.uk

OxPoints

Ord-nance Survey

Openly Local

Open Library

OpenCyc

Open Corpo-rates

OpenCalais

OpenEI

Open Election

Data Project

OpenData

Thesau-rus

Ontos News Portal

OGOLOD

JanusAMP

Ocean Drilling Codices

New York

Times

NVD

ntnusc

NTU Resource

Lists

Norwe-gian

MeSH

NDL subjects

ndlna

myExperi-ment

Italian Museums

medu-cator

MARC Codes List

Man-chester Reading

Lists

Lotico

Weather Stations

London Gazette

LOIUS

Linked Open Colors

lobidResources

lobidOrgani-sations

LEM

LinkedMDB

LinkedLCCN

LinkedGeoData

LinkedCT

LinkedUser

FeedbackLOV

Linked Open

Numbers

LODE

Eurostat (OntologyCentral)

Linked EDGAR

(OntologyCentral)

Linked Crunch-

base

lingvoj

Lichfield Spen-ding

LIBRIS

Lexvo

LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

Klapp-stuhl-club

Good-win

Family

National Radio-activity

JP

Jamendo (DBtune)

Italian public

schools

ISTAT Immi-gration

iServe

IdRef Sudoc

NSZL Catalog

Hellenic PD

Hellenic FBD

PiedmontAccomo-dations

GovTrack

GovWILD

GoogleArt

wrapper

gnoss

GESIS

GeoWordNet

GeoSpecies

GeoNames

GeoLinkedData

GEMET

GTAA

STITCH

SIDER

Project Guten-berg

MediCare

Euro-stat

(FUB)

EURES

DrugBank

Disea-some

DBLP (FU

Berlin)

DailyMed

CORDIS(FUB)

Freebase

flickr wrappr

Fishes of Texas

Finnish Munici-palities

ChEMBL

FanHubz

EventMedia

EUTC Produc-

tions

Eurostat

Europeana

EUNIS

EU Insti-

tutions

ESD stan-dards

EARTh

Enipedia

Popula-tion (En-AKTing)

NHS(En-

AKTing) Mortality(En-

AKTing)

Energy (En-

AKTing)

Crime(En-

AKTing)

CO2 Emission

(En-AKTing)

EEA

SISVU

education.data.g

ov.uk

ECS South-ampton

ECCO-TCP

GND

Didactalia

DDC Deutsche Bio-

graphie

datadcs

MusicBrainz

(DBTune)

Magna-tune

John Peel

(DBTune)

Classical (DB

Tune)

AudioScrobbler (DBTune)

Last.FM artists

(DBTune)

DBTropes

Portu-guese

DBpedia

dbpedia lite

Greek DBpedia

DBpedia

data-open-ac-uk

SMCJournals

Pokedex

Airports

NASA (Data Incu-bator)

MusicBrainz(Data

Incubator)

Moseley Folk

Metoffice Weather Forecasts

Discogs (Data

Incubator)

Climbing

data.gov.uk intervals

Data Gov.ie

databnf.fr

Cornetto

reegle

Chronic-ling

America

Chem2Bio2RDF

Calames

businessdata.gov.

uk

Bricklink

Brazilian Poli-

ticians

BNB

UniSTS

UniPathway

UniParc

Taxonomy

UniProt(Bio2RDF)

SGD

Reactome

PubMedPub

Chem

PRO-SITE

ProDom

Pfam

PDB

OMIMMGI

KEGG Reaction

KEGG Pathway

KEGG Glycan

KEGG Enzyme

KEGG Drug

KEGG Com-pound

InterPro

HomoloGene

HGNC

Gene Ontology

GeneID

Affy-metrix

bible ontology

BibBase

FTS

BBC Wildlife Finder

BBC Program

mes BBC Music

Alpine Ski

Austria

LOCAH

Amster-dam

Museum

AGROVOC

AEMET

US Census (rdfabout)

Media

Geographic

Publications

Government

Cross-domain

Life sciences

User-generated content

September ’11:295 datasets, 25B

triples

11PKU/2014-08-28





April ’14:1091 datasets, ???

triples

11PKU/2014-08-28

Max Schmachtenberg, Christian Bizer, and Heiko Paulheim: Adoption of LinkedData Best Practices in Different Topical Domains. In Proc. ISWC, 2014.

Closer Look

12PKU/2014-08-28

Globally Distributed Network of Data

13PKU/2014-08-28

Three Approaches

Data warehousing

Consolidate data in a repository and query it

SPARQL federation

Leverage query services provided by data publishers

Live Linked Data querying

Navigate through LOD by looking up URIs at query executiontime

14PKU/2014-08-28

Outline

1 LOD and RDF Introduction

2 Data Warehousing ApproachRelational ApproachesGraph-Based Approaches

3 SPARQL Federation ApproachDistributed RDF ProcessingSPARQL Endpoint Federation

4 Live Querying ApproachTraversal-based approachesIndex-based approachesHybrid approaches

5 Conclusions

15PKU/2014-08-28

Outline





5 Conclusions

16PKU/2014-08-28

Traditional Hypertext-based Web Access

IMDb WorldBook

Data exposedto the Webvia HTML

17PKU/2014-08-28

Linked Data Publishing Principles

IMDb WorldBook

(http://...linkedmdb.../Shining,releaseDate, 23 May 1980)(http://...linkedmdb.../Shining, filmLocation, http://cia.../UK)(http://...linkedmdb.../29704,actedIn, http://...linkedmdb.../Shining)

...

(http://cia.../UK, hasPopulation, 63230000)...

Shi

ning

UK

Data model: RDFGlobal identifier: URIAccess mechanism: HTTPConnection: data links

18PKU/2014-08-28

RDF Introduction

Everything is an uniquely namedresource

Prefixes can be used to shorten thenames

Properties of resources can bedefined

Relationships with other resourcescan be defined

Resource descriptions can becontributed by differentpeople/groups and can be locatedanywhere in the web

Integrated web “database”

http://data.linkedmdb.org/resource/actor/JN29704

xmlns:y=http://data.linkedmdb.org/resource/actor/y:JN29704

y:JN29704:hasName “Jack Nicholson”y:JN29704:BornOnDate “1937-04-22”

y:TS2014:title “The Shining”y:TS2014:releaseDate “1980-05-23”

JN29704:movieActor

y:TS2014

19PKU/2014-08-28

RDF Data Model

Triple: Subject, Predicate (Property),Object (s, p, o)

Subject: the entity that is described(URI or blank node)

Predicate: a feature of the entity (URI)Object: value of the feature (URI,

blank node or literal)

(s, p, o) ∈ (U ∪ B)× U × (U ∪ B ∪ L)

Set of RDF triples is called an RDF graph

U

Subject Object

U B U B L

U: set of URIsB: set of blank nodesL: set of literals

Predicate

Subject Predicate Objecthttp://...imdb.../film/2014 rdfs:label “The Shining”http://...imdb.../film/2014 movie:releaseDate “1980-05-23”http://...imdb.../29704 movie:actor name “Jack Nicholson”. . . . . . . . .

20PKU/2014-08-28

RDF Example InstancePrefixes: mdb=http://data.linkedmdb.org/resource/; geo=http://sws.geonames.org/

bm=http://wifo5-03.informatik.uni-mannheim.de/bookmashup/lexvo=http://lexvo.org/id/;wp=http://en.wikipedia.org/wiki/

Subject Predicate Object

mdb: film/2014 rdfs:label “The Shining”mdb:film/2014 movie:initial release date “1980-05-23”’mdb:film/2014 movie:director mdb:director/8476mdb:film/2014 movie:actor mdb:actor/29704mdb:film/2014 movie:actor mdb: actor/30013mdb:film/2014 movie:music contributor mdb: music contributor/4110mdb:film/2014 foaf:based near geo:2635167mdb:film/2014 movie:relatedBook bm:0743424425mdb:film/2014 movie:language lexvo:iso639-3/engmdb:director/8476 movie:director name “Stanley Kubrick”mdb:film/2685 movie:director mdb:director/8476mdb:film/2685 rdfs:label “A Clockwork Orange”mdb:film/424 movie:director mdb:director/8476mdb:film/424 rdfs:label “Spartacus”mdb:actor/29704 movie:actor name “Jack Nicholson”mdb:film/1267 movie:actor mdb:actor/29704mdb:film/1267 rdfs:label “The Last Tycoon”mdb:film/3418 movie:actor mdb:actor/29704mdb:film/3418 rdfs:label “The Passenger”geo:2635167 gn:name “United Kingdom”geo:2635167 gn:population 62348447geo:2635167 gn:wikipediaArticle wp:United Kingdombm:books/0743424425 dc:creator bm:persons/Stephen+Kingbm:books/0743424425 rev:rating 4.7bm:books/0743424425 scom:hasOffer bm:offers/0743424425amazonOfferlexvo:iso639-3/eng rdfs:label “English”lexvo:iso639-3/eng lvont:usedIn lexvo:iso3166/CAlexvo:iso639-3/eng lvont:usesScript lexvo:script/Latn

URI Literal

URI

21PKU/2014-08-28

RDF Graph

mdb:film/2014

“1980-05-23”

movie:initial release date

“The Shining”refs:label

bm:books/0743424425

4.7

rev:rating

bm:offers/0743424425amazonOffer

geo:2635167

“United Kingdom”

gn:name

62348447

gn:population

mdb:actor/29704


movie:actor name

mdb:film/3418

“The Passenger”

refs:label

mdb:film/1267

“The Last Tycoon”

refs:label

mdb:director/8476


movie:director name

mdb:film/2685

“A Clockwork Orange”

refs:label

mdb:film/424

“Spartacus”

refs:label

mdb:actor/30013

movie:relatedBook

scam:hasOffer

foaf:based nearmovie:actor

movie:directormovie:actor

movie:actor movie:actor

movie:director movie:director

22PKU/2014-08-28

Linked Data Model [Hartig, 2012]

Web Document

Given a countably infinite set D (documents), a Web of LinkedData is a tuple W = (D, adoc, data) where:

I D ⊆ D,

I adoc is a partial mapping from URIs to D, and

I data is a total mapping from D to finite sets of RDF triples.

23PKU/2014-08-28

Linked Data Model [Hartig, 2012]

Web Document

Given a countably infinite set D (documents), a Web of LinkedData is a tuple W = (D, adoc, data) where:

I D ⊆ D,

I adoc is a partial mapping from URIs to D, and

I data is a total mapping from D to finite sets of RDF triples.

Web of Linked Data

A Web of Linked Data W = (D, adoc, data)contains a data link from document d ∈ D todocument d ′ ∈ D if there exists a URI u suchthat:

I u is mentioned in an RDF triplet ∈ data(d), and

I d ′ = adoc(u).23PKU/2014-08-28

RDF Query Model – SPARQLQuery Model - SPARQL Protocol and RDF Query LanguageGiven U (set of URIs), L (set of literals), and V (set ofvariables), a SPARQL expression is defined recursively:

an atomic triple pattern, which is an element of

(U ∪ V )× (U ∪ V )× (U ∪ V ∪ L)

?x rdfs:label “The Shining”

P FILTER R, where P is a graph pattern expression and R is abuilt-in SPARQL condition (i.e., analogous to a SQL predicate)

?x rev:rating ?p FILTER(?p > 3.0)

P1 AND/OPT/UNION P2, where P1 and P2 are graphpattern expressions

Example:SELECT ?nameWHERE {

?m r d f s : l a b e l ?name . ?m movie : d i r e c t o r ?d .?d movie : d i r e c t o r n a m e ” S t a n l e y K u b r i c k ” .?m movie : r e l a t e d B o o k ?b . ?b r e v : r a t i n g ? r .FILTER(? r > 4 . 0 )

}24PKU/2014-08-28

SPARQL Queries

SELECT ?nameWHERE {


}

?m ?dmovie:director

?name

rdfs:label

?b

movie:relatedBook


movie:director name

?rrev:rating

FILTER(?r > 4.0)

25PKU/2014-08-28

Outline





5 Conclusions

26PKU/2014-08-28

Naıve Triple Store Design

SELECT ?nameWHERE {


}Subject Property Objectmdb:film/2014 rdfs:label “The Shining”mdb:film/2014 movie:initial release date “1980-05-23”mdb:film/2014 movie:director mdb:director/8476mdb:film/2014 movie:actor mdb:actor/29704mdb:film/2014 movie:actor mdb: actor/30013mdb:film/2014 movie:music contributor mdb: music contributor/4110mdb:film/2014 foaf:based near geo:2635167mdb:film/2014 movie:relatedBook bm:0743424425mdb:film/2014 movie:language lexvo:iso639-3/engmdb:director/8476 movie:director name “Stanley Kubrick”mdb:film/2685 movie:director mdb:director/8476mdb:film/2685 rdfs:label “A Clockwork Orange”mdb:film/424 movie:director mdb:director/8476mdb:film/424 rdfs:label “Spartacus”mdb:actor/29704 movie:actor name “Jack Nicholson”mdb:film/1267 movie:actor mdb:actor/29704mdb:film/1267 rdfs:label “The Last Tycoon”mdb:film/3418 movie:actor mdb:actor/29704mdb:film/3418 rdfs:label “The Passenger”geo:2635167 gn:name “United Kingdom”geo:2635167 gn:population 62348447geo:2635167 gn:wikipediaArticle wp:United Kingdombm:books/0743424425 dc:creator bm:persons/Stephen+Kingbm:books/0743424425 rev:rating 4.7bm:books/0743424425 scom:hasOffer bm:offers/0743424425amazonOfferlexvo:iso639-3/eng rdfs:label “English”lexvo:iso639-3/eng lvont:usedIn lexvo:iso3166/CAlexvo:iso639-3/eng lvont:usesScript lexvo:script/Latn

Easy to implementbut

too many self-joins!

27PKU/2014-08-28

Naıve Triple Store Design

SELECT ?nameWHERE {


}Subject Property Objectmdb:film/2014 rdfs:label “The Shining”mdb:film/2014 movie:initial release date “1980-05-23”mdb:film/2014 movie:director mdb:director/8476mdb:film/2014 movie:actor mdb:actor/29704mdb:film/2014 movie:actor mdb: actor/30013mdb:film/2014 movie:music contributor mdb: music contributor/4110mdb:film/2014 foaf:based near geo:2635167mdb:film/2014 movie:relatedBook bm:0743424425mdb:film/2014 movie:language lexvo:iso639-3/engmdb:director/8476 movie:director name “Stanley Kubrick”mdb:film/2685 movie:director mdb:director/8476mdb:film/2685 rdfs:label “A Clockwork Orange”mdb:film/424 movie:director mdb:director/8476mdb:film/424 rdfs:label “Spartacus”mdb:actor/29704 movie:actor name “Jack Nicholson”mdb:film/1267 movie:actor mdb:actor/29704mdb:film/1267 rdfs:label “The Last Tycoon”mdb:film/3418 movie:actor mdb:actor/29704mdb:film/3418 rdfs:label “The Passenger”geo:2635167 gn:name “United Kingdom”geo:2635167 gn:population 62348447geo:2635167 gn:wikipediaArticle wp:United Kingdombm:books/0743424425 dc:creator bm:persons/Stephen+Kingbm:books/0743424425 rev:rating 4.7bm:books/0743424425 scom:hasOffer bm:offers/0743424425amazonOfferlexvo:iso639-3/eng rdfs:label “English”lexvo:iso639-3/eng lvont:usedIn lexvo:iso3166/CAlexvo:iso639-3/eng lvont:usesScript lexvo:script/Latn

SELECT T1 . o b j e c tFROM T as T1 , T as T2 , T as T3 ,

T as T4 , T as T5WHERE T1 . p=” r d f s : l a b e l ”AND T2 . p=” movie : r e l a t e d B o o k ”AND T3 . p=” movie : d i r e c t o r ”AND T4 . p=” r e v : r a t i n g ”AND T5 . p=” movie : d i r e c t o r n a m e ”AND T1 . s=T2 . sAND T1 . s=T3 . sAND T2 . o=T4 . sAND T3 . o=T5 . sAND T4 . o > 4 . 0AND T5 . o=” S t a n l e y K u b r i c k ”

Easy to implementbut

too many self-joins!

27PKU/2014-08-28

Exhaustive Indexing

RDF-3X [Neumann and Weikum, 2008, 2009], Hexastore[Weiss et al., 2008]Strings are mapped to ids using a mapping table

Triples are indexed in a clustered B+ tree in lexicographicorderCreate indexes for permutations of the three columns: SPO,SOP, PSO, POS, OPS, OSP

Original triple tableSubject Property Objectmdb: film/2014 rdfs:label “The Shining”mdb:film/2014 movie:initial release date “1980-05-23”mdb:director/8476 movie:director name “Stanley Kubrick”mdb:film/2685 movie:director mdb:director/8476

Encoded triple tableSubject Property Object

0 1 20 3 45 6 78 9 5

Mapping tableID Value0 mdb: film/20141 rdfs:label2 “The Shining”3 movie:initial release date4 “1980-05-23”5 mdb:director/84766 movie:director name7 “Stanley Kubrick”8 mdb:film/26859 movie:director

28PKU/2014-08-28

Exhaustive Indexing

RDF-3X [Neumann and Weikum, 2008, 2009], Hexastore[Weiss et al., 2008]

Strings are mapped to ids using a mapping table

Triples are indexed in a clustered B+ tree in lexicographicorder

Create indexes for permutations of the three columns: SPO,SOP, PSO, POS, OPS, OSP

Subject Property Object0 1 2

0 3 4

5 6 7

8 9 5...

......

B+ treeEasy queryingthrough mappingtable

28PKU/2014-08-28

Exhaustive Indexing–Query Execution

Each triple pattern can be answered by a range query

Joins between triple patterns computed using merge join

Join order is easy due to extensive indexing


0 3 4

5 6 7

8 9 5...

......

ID Value0 mdb: film/2014

1 rdfs:label

2 “The Shining”

3 movie:initial release date

4 “1980-05-23”

5 mdb:director/8476

6 movie:director name

7 “Stanley Kubrick”

8 mdb:film/2685

9 movie:director

29PKU/2014-08-28






0 3 4

5 6 7

8 9 5...

......


1 rdfs:label

2 “The Shining”


4 “1980-05-23”

5 mdb:director/8476



8 mdb:film/2685

9 movie:director

Advantages

I Eliminates some of the joins – they become range queries

I Merge join is easy and fast

29PKU/2014-08-28






0 3 4

5 6 7

8 9 5...

......


1 rdfs:label

2 “The Shining”


4 “1980-05-23”

5 mdb:director/8476



8 mdb:film/2685

9 movie:director

Advantages

I Eliminates some of the joins – they become range queries

I Merge join is easy and fast

Disadvantages

I Space usage

29PKU/2014-08-28

Property Tables

Grouping by entities; Jena [Wilkinson, 2006], DB2-RDF[Bornea et al., 2013]

Clustered property table: group together the properties thattend to occur in the same (or similar) subjects

Property-class table: cluster the subjects with the same typeof property into one property table

Subject Property Objectmdb:film/2014 rdfs:label “The Shining”mdb:film/2014 movie:director mdb:director/8476mdb:film/2685 movie:director mdb:director/8476mdb:film/2685 rdfs:label “A Clockwork Orange”mdb:actor/29704 movie:actor name “Jack Nicholson”. . . . . . . . .

Subject refs:label movie:directormob:film/2014 “The Shining” mob:director/8476mob:film/2685 “The Clockwork Orange” mob:director/8476

Subject movie:actor namemdb:actor “Jack Nicholson”

30PKU/2014-08-28

Property Tables







Advantages

I Fewer joins

I If the data is structured, we have a relational system – similarto normalized relations

30PKU/2014-08-28

Property Tables







Advantages

I Fewer joins

I If the data is structured, we have a relational system – similarto normalized relations

Disadvantages

I Potentially a lot of NULLs

I Clustering is not trivial

I Multi-valued properties are complicated

30PKU/2014-08-28

Binary Tables

Grouping by properties: For each property, build a two-columntable, containing both subject and object, ordered by subjects[Abadi et al., 2007, 2009]

Also called vertical partitioned tables

n two column tables (n is the number of unique properties inthe data)


Subject Objectmdb:film/2014 mdb:director/8476mdb:film/2685 mdb:director/8476

movie:director

Subject Objectmob:film/2014 “The Shining”mob:film/2685 “The Clockwork Orange”

refs:label

Subject Objectmdb:actor/29704 “Jack Nicholson”

movie:actor name

31PKU/2014-08-28

Binary Tables






movie:director


refs:label


movie:actor name

Advantages

I Supports multi-valued properties

I No NULLs

I No clustering

I Read only needed attributes (i.e. less I/O)

I Good performance for subject-subject joins

31PKU/2014-08-28

Binary Tables






movie:director


refs:label


movie:actor name

Advantages

I Supports multi-valued properties

I No NULLs

I No clustering

I Read only needed attributes (i.e. less I/O)

I Good performance for subject-subject joins

Disadvantages

I Not useful for subject-object joins

I Expensive inserts

31PKU/2014-08-28

Graph-based Approach

Answering SPARQL query ≡ subgraph matching

gStore [Zou et al., 2011, 2014]

?m ?dmovie:director

?name

rdfs:label

?b

movie:relatedBook


movie:director name

?rrev:rating

FILTER(?r > 4.0)

mdb:film/2014

“1980-05-23”



bm:books/0743424425

4.7

rev:rating


geo:2635167


gn:name

62348447

gn:population

mdb:actor/29704


movie:actor name

mdb:film/3418

“The Passenger”

refs:label

mdb:film/1267


refs:label

mdb:director/8476


movie:director name

mdb:film/2685


refs:label

mdb:film/424

“Spartacus”

refs:label

mdb:actor/30013

movie:relatedBook

scam:hasOffer





SubgraphM

atching

32PKU/2014-08-28




?m ?dmovie:director

?name

rdfs:label

?b

movie:relatedBook


movie:director name

?rrev:rating

FILTER(?r > 4.0)

mdb:film/2014

“1980-05-23”



bm:books/0743424425

4.7

rev:rating


geo:2635167


gn:name

62348447

gn:population

mdb:actor/29704


movie:actor name

mdb:film/3418

“The Passenger”

refs:label

mdb:film/1267


refs:label

mdb:director/8476


movie:director name

mdb:film/2685


refs:label

mdb:film/424

“Spartacus”

refs:label

mdb:actor/30013

movie:relatedBook

scam:hasOffer





SubgraphM

atching

Advantages

I Maintains the graph structure

I Full set of queries can be handled

32PKU/2014-08-28




?m ?dmovie:director

?name

rdfs:label

?b

movie:relatedBook


movie:director name

?rrev:rating

FILTER(?r > 4.0)

mdb:film/2014

“1980-05-23”



bm:books/0743424425

4.7

rev:rating


geo:2635167


gn:name

62348447

gn:population

mdb:actor/29704


movie:actor name

mdb:film/3418

“The Passenger”

refs:label

mdb:film/1267


refs:label

mdb:director/8476


movie:director name

mdb:film/2685


refs:label

mdb:film/424

“Spartacus”

refs:label

mdb:actor/30013

movie:relatedBook

scam:hasOffer





SubgraphM

atching

Advantages

I Maintains the graph structure

I Full set of queries can be handled

Disadvantages

I Graph pattern matching is expensive

32PKU/2014-08-28

gStore

General Approach:

Work directly on the RDF graph and the SPARQL query graph

Use a signature-based encoding of each entity and class vertexto speed up matching

Filter-and-evaluate

Use a false positive algorithm to prune nodes and obtain a setof candidates; then do more detailed evaluation on those

Use an index (VS∗-tree) over the data signature graph (haslight maintenance load) for efficient pruning

33PKU/2014-08-28

1. Encode Q and G to Get Signature GraphsQuery signature graph Q∗

0100 0000 1000 000000010

0000 010010000

Data signature graph G∗

0010 1000

0100 0001

00001

1000 000100010

0000 0100

10000

0000 1000

10000

0000 0010

10000

0000 1001

00100

0001 000101000

0100 1000

01000

1001 1000

01000

0001 0100

01000

34PKU/2014-08-28

2. Filter-and-EvaluateQuery signature graph Q∗

0100 0000 1000 000000010

0000 010010000

Data signature graph G∗

0010 1000

0100 0001

00001

1000 000100010

0000 0100

10000

0000 1000

10000

0000 0010

10000

0000 1001

00100

0001 000101000

0100 1000

01000

1001 1000

01000

0001 0100

01000

Find matches of Q∗ oversignature graph G ∗

Verify each match inRDF graph G

35PKU/2014-08-28

How to Generate Candidate List

Two step process:1. For each node of Q∗ get lists of nodes in G∗ that include that

node.2. Do a multi-way join to get the candidate list

Alternatives:

Sequential scan of G∗

Both steps are inefficient

Use S-treesHeight-balanced tree over signaturesRun an inclusion query for each node of Q∗ and get lists ofnodes in G∗ that include that node.

• Given query signature q and a set of data signatures S ,find all data signatures si ∈ S where q&si = q

Does not support second step – expensive

VS-tree (and VS∗-tree)Multi-resolution summary graph based on S-treeSupports both steps efficientlyGrouping by vertices

36PKU/2014-08-28

How to Generate Candidate List

Two step process:1. For each node of Q∗ get lists of nodes in G∗ that include that

node.2. Do a multi-way join to get the candidate list

Alternatives:Sequential scan of G∗

Both steps are inefficient

Use S-treesHeight-balanced tree over signaturesRun an inclusion query for each node of Q∗ and get lists ofnodes in G∗ that include that node.

• Given query signature q and a set of data signatures S ,find all data signatures si ∈ S where q&si = q

Does not support second step – expensive

VS-tree (and VS∗-tree)Multi-resolution summary graph based on S-treeSupports both steps efficientlyGrouping by vertices

36PKU/2014-08-28

S-tree Solution

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

1000 00000100 000000010

0000 010010000

Possibly large join space!

37PKU/2014-08-28

S-tree Solution

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

1000 00000100 000000010

0000 010010000 002

011


37PKU/2014-08-28

S-tree Solution

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

1000 00000100 000000010

0000 010010000 002

011

003

008


37PKU/2014-08-28

S-tree Solution

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

1000 00000100 000000010

0000 010010000 002

011

003

008

004

009


37PKU/2014-08-28

S-tree Solution

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

1000 00000100 000000010

0000 010010000 002

011

003

008

004

009on on


37PKU/2014-08-28

VS-tree

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

11101

1001010001 01100

10000 00001 01100

00010

10000

01000

01000

10000

10000

10000

1000000010

00100

01000

01000

01000

01000

Super edge

38PKU/2014-08-28

Pruning with VS-Tree

1111 1111

0110 1111 1101 1101

0000 1110 0110 1001 1100 1001 1001 1101

0000 1000

0000 0100 0000 0010

0010 1000

0100 0001

1000 0001

0000 1001

0100 1000

1001 1000

0001 0100

0001 0001

005

004 006

001

002

003

007

011

008

009

010

d11

d21 d2

2

d31 d3

2 d33 d3

4

G 3

G 2

G 1

11101

1001010001 01100

10000 00001 01100

00010

10000

01000

01000

10000

10000

10000

1000000010

00100

01000

01000

01000

01000

1000 00000100 000000010

0000 010010000

d32

d33

d33

d34

d31

d34

G 3

00010 10000

01000

003

008

002

011

004

009onon

Reduced join space!

39PKU/2014-08-28

Adaptivity to Workload

Web applications that are supported by RDF datamanagement systems are far more varied than conventionalrelational applications

Data that are being handled are far more heterogeneous

SPARQL is far more flexible in how triple patterns (i.e., theatomic query unit) can be combined

An experiment [Aluc et al., 2014]

RDF-3X VOS (6.1) VOS (7.1) MonetDB 4Store% queries for whichtested system isfastest

20.9 0.0 22.6 56.5 0.0

Total workload exe-cution time (hours)

27.1 20.9 20.8 38.6 72.2

Mean (per query)execution time (sec-onds)

7.8 6.0 6.0 11.1 20.7

40PKU/2014-08-28

Adaptivity to Workload

Web applications that are supported by RDF datamanagement systems are far more varied than conventionalrelational applications

Data that are being handled are far more heterogeneous

SPARQL is far more flexible in how triple patterns (i.e., theatomic query unit) can be combined

An experiment [Aluc et al., 2014]

RDF-3X VOS (6.1) VOS (7.1) MonetDB 4Store% queries for whichtested system isfastest

20.9 0.0 22.6 56.5 0.0

Total workload exe-cution time (hours)

27.1 20.9 20.8 38.6 72.2

Mean (per query)execution time (sec-onds)

7.8 6.0 6.0 11.1 20.7

Summary of Experiments

I No single system is a sole winner across all queries

I No single system is the sole loser across all queries, either

I There can be 2–5 orders of magnitude difference in the performance (i.e., queryexecution time) between the best and the worst system for a given query

I The winner in one query may timeout in another

I Performance difference widens as dataset size increases

40PKU/2014-08-28

Group-by-Query Approach

Tamer Post23571hasPost

OlaftaggedIn

UWaterlooworksAt

Tamer Post23hasPost

Boblikes

UWaterlooworksAt

Post2hasPost taggedIn

Tamer Post23hasPost

BobtaggedIn

UWaterlooworksAt

Post2hasPost favourites

41PKU/2014-08-28

Challenges

Group-by-query clusters (a) do not have fixed size, (b) containsame set of attributes

1. Workload time analysis

2. Updating the physical layout

3. Partial indexing

Type-A,robust

Type-C,robust

Type-A,adaptable

Type-B,adaptable

Type-B,

adaptable

Type-B,

adaptable

Type-B,adaptable T

ype-C,

adaptable

42PKU/2014-08-28

Challenges




3. Partial indexing

Storage System

CacheHash

Function

evict

@t1

· · ·

functionadapts

HashFunction

@tk

42PKU/2014-08-28

Challenges




3. Partial indexing

Storage System

CacheHash

Function

evict

@t1

· · ·

functionadapts

HashFunction

@tk

Index – – – – – – – – – –

SPARQL Query Engine

42PKU/2014-08-28

chameleon-db

Prototype system [Aluc et al., 2013]

35,000 lines of code in C++ and growing

Structural Index

...

Vertex Index

Spill Index

Clu

ster

Inde

xS

tora

geS

yste

m Sto

rage

Adv

isor

QueryEngine Plan Generation Evaluation

43PKU/2014-08-28

Some Open Problems

Scalability of the solutions to very large datasets

Maintenance of auxiliary data structures in dynamicenvironments

Adaptive systems to handle varying and time-changingworkloads

Uncertain RDF data processing

Keyword search over RDF data

Query processing over incomplete RDF data

44PKU/2014-08-28

Outline





5 Conclusions

45PKU/2014-08-28

Remember the Environment

Distributed environment

Some of the data sites canprocess SPARQL queries –SPARQL endpoints

Not all data sites canprocess queries

Alternatives

Data re-distribution +query decompositionSPARQL federation: justprocess at SPARQLendpointsLive querying (see nextsection)

46PKU/2014-08-28





Alternatives

Data re-distribution +query decomposition

SPARQL federation: justprocess at SPARQLendpointsLive querying (see nextsection)

46PKU/2014-08-28





Alternatives

Data re-distribution +query decompositionSPARQL federation: justprocess at SPARQLendpoints

Live querying (see nextsection)

46PKU/2014-08-28





Alternatives

Data re-distribution +query decompositionSPARQL federation: justprocess at SPARQLendpointsLive querying (see nextsection)

46PKU/2014-08-28

Distributed RDF Processing [Kaoudi and Manolescu, 2014]

Data partitioning approachesRDF data warehouse is partitioned and distributed

RDF data D = {D1, . . . ,Dn}Allocate each Di to a site

Partitioning alternativesTable-based (e.g., [Husain et al., 2011])Graph-based (e.g., [Huang et al., 2011; Zhang et al., 2013])Unit-based (e.g., [Gurajada et al., 2014; Lee and Liu, 2013])

SPARQL query decomposed Q = {Q1, . . . ,Qk}Distributed execution of {Q1, . . . ,Qk} over {D1, . . . ,Dn}

I High performance

I Great for parallelizing centralized RDF data

I May not be possible to re-partition and re-allocate Web data(i.e., LOD)

47PKU/2014-08-28

Distributed RDF Processing – 2

Data summary-based approaches

Build summaries (index) for the distributed RDF datasets(e.g., [Atre et al., 2010; Prasser et al., 2012])

SPARQL query Q = {Q1, . . . ,Qk}Distributed execution of {Q1, . . . ,Qk} using the datasummary

I No data re-partitioning and re-allocation

I Have to scan the data at each site

I Index over distributed data with maintenance concerns

48PKU/2014-08-28

SPARQL Endpoint Federation

Consider only the SPARQL endpoints for query execution

No data re-partitioning/re-distribution

Consider D = D1 ∪ D2 ∪ . . . ∪ Dn; Di : SPARQL endpoint

AlternativesSPARQL query decomposed Q = {Q1, . . . ,Qk} and executedover {D1, . . . ,Dn} – DARQ, FedX [Schwarte et al., 2011],SPLENDID [Gorlitz and Staab, 2011], ANAPSID [Acostaet al., 2011]Partial query evaluation – Distributed gStore [Peng et al.,2014]

Partial evaluation

I Given function f (s, d) and part of its input s, perform f ’scomputation that only depends on s to get f ′(d)

I Compute f ′(d) when d becomes available

I Applied to, e.g., XML [Buneman et al., 2006]

49PKU/2014-08-28

SPARQL Endpoint Federation

Consider only the SPARQL endpoints for query execution

No data re-partitioning/re-distribution

Consider D = D1 ∪ D2 ∪ . . . ∪ Dn; Di : SPARQL endpointAlternatives

SPARQL query decomposed Q = {Q1, . . . ,Qk} and executedover {D1, . . . ,Dn} – DARQ, FedX [Schwarte et al., 2011],SPLENDID [Gorlitz and Staab, 2011], ANAPSID [Acostaet al., 2011]Partial query evaluation – Distributed gStore [Peng et al.,2014]

Partial evaluation

I Given function f (s, d) and part of its input s, perform f ’scomputation that only depends on s to get f ′(d)

I Compute f ′(d) when d becomes available

I Applied to, e.g., XML [Buneman et al., 2006]

49PKU/2014-08-28

Distributed SPARQL Using Partial Query EvaluationTwo steps:

1. Evaluate a query at each site to find local matchesQuery is the function and each Di is the known inputInner match or local partial match

2. Assemble the partial matches to get final resultCrossing matchCentralized assemblyDistributed assembly

D1

D2

D3

D4

Crossing match

50PKU/2014-08-28

Some Open Problems

Handling data at non-SPARQL endpoint sites

Modification to SPARQL endpoints (for partial queryevaluation)

Heterogeneous use of vocabularies (use of ontologies)

51PKU/2014-08-28

Outline





5 Conclusions

52PKU/2014-08-28

Live Query Processing

Not all data resides atSPARQL endpoints

Freshness of access to dataimportant

Potentially countably infinitedata sources

Live querying

On-line executionOnly rely on linked dataprinciples

Alternatives

Traversal-basedapproachesIndex-based approachesHybrid approaches

53PKU/2014-08-28

SPARQL Query Semantics in Live Querying

Full-web semantics

Scope of evaluating a SPARQL expression is all Linked DataQuery result completeness cannot be guaranteed by any(terminating) execution

Reachability-based query semantics

Query consists of a SPARQL expression, a set of seed URIs S ,and a reachability condition cScope: all data along paths of data links that satisfy theconditionComputationally feasible

54PKU/2014-08-28

Traversal Approaches

Discover relevant URIs recursivelyby traversing (specific) data linksat query execution runtime [Hartig,2013; Ladwig and Tran, 2011]

Implements reachability-basedquery semantics

Start from a set of seed URIsRecursively follow and discovernew URIs

Important issue is selection of seedURIs

Retrieved data serves to discovernew URIs and to construct result

55PKU/2014-08-28







Advantages

Easy to implement.No data structure to maintain.

55PKU/2014-08-28







Advantages

Easy to implement.No data structure to maintain.

Disadvantages

Possibilities for parallelized data retrieval are limitedRepeated data retrieval introduces significant query latency.

55PKU/2014-08-28

Traversal Optimization

Dynamic query execution [Hartig and Ozsu, 2014]

...lookup queue...

Data Retrieval

Output

56PKU/2014-08-28

Traversal Optimization

Dynamic query execution [Hartig and Ozsu, 2014]

Prioritization of URIs – a number of alternatives

Non-adaptiveAdaptive,

Local processing awareAdaptive,

Local processing agnostic

Intermediate solution driven Solution-aware graph-based

Hybrid graph-based Purely graph-based

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Index Approaches

Use pre-populated index to determine relevant URIs (and toavoid as many irrelevant ones as possible)

Different index keys possible; e.g., triple patterns [Umbrichet al., 2011]

Index entries a set of URIsIndexed URIs may appear multiple times (i.e., associated withmultiple index keys)Each URI in such an entry may be paired with a cardinality(utilized for source ranking)

Key: tp Entry: {uri1, uri2, , urin}

GET urii

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Index Approaches





GET urii

Advantages

Data retrieval can be fully parallelizedReduces the impact of data retrieval on query execution time

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Index Approaches





GET urii

Advantages

Data retrieval can be fully parallelizedReduces the impact of data retrieval on query execution time

Disadvantages

Querying can only start after index constructionDepends on what has been selected for the indexFreshness may be an issueIndex maintenance

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Hybrid Approach

Perform a traversal-based execution using a prioritized list ofURIs to look up [Ladwig and Tran, 2010]

Initial seed from the pre-populated index

Non-seed URIs are ranked by a function based on informationin the index

New discovered URIs that are not in the index are rankedaccording to number of referring documents

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Some Open Problems

Optimize queries by using statistics collected during earlierquery executions

Heterogeneous use of vocabularies (use of ontologies)

Combine SPARQL federation to leverage SPARQL endpointfunctionality

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Outline





5 Conclusions

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Conclusions

RDF and Linked Object Data seem to have considerablepromise for Web data management

More work needs to be done

Query semanticsAdaptive system designOptimizations – both in data warehousing and distributedenvironmentsLive querying requires significant thought to reduce latency

2014 2011

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Conclusions

What I did not talk about:

Not much on general distributed/parallel processing

Not much on SPARQL semantics

Nothing about RDFS – no schema stuff

Nothing about entailment regimes > 0⇒ no reasoning

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Thank you!

Research supported by

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web data management with rdf

Technology

similar data

rdf data volumes

fastlinked data cloud

web queryingfusion tablesusers

rdf datae

rdf usesyago

wikipediarepresentas

rdf agem