Web Data Management in RDF Age
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Transcript of Web Data Management in RDF Age
Web Data Management in RDF Age
M. Tamer Ozsu
University of WaterlooDavid R. Cheriton School of Computer Science
1Inria/2014-10-01
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
3Inria/2014-10-01
Web Data Management
A long term research interest in the DB community
2000 2004
2011 20114Inria/2014-10-01
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?
5Inria/2014-10-01
More Recent Approaches to Web Querying
Fusion TablesUsers contribute data in spreadsheet, CVS, KML formatPossible joins between multiple data setsExtensive visualization
8Inria/2014-10-01
More Recent Approaches to Web Querying
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
“Jack Nicholson”
8Inria/2014-10-01
More Recent Approaches to Web Querying
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)
8Inria/2014-10-01
RDF Data Volumes . . .
. . . are growing – and fast
Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year
11Inria/2014-10-01
RDF Data Volumes . . .
. . . are growing – and fast
Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year
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
11Inria/2014-10-01
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.http://lod-cloud.net/
RDF Data Volumes . . .
. . . are growing – and fast
Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year
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
11Inria/2014-10-01
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.http://lod-cloud.net/
RDF Data Volumes . . .
. . . are growing – and fast
Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year
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
11Inria/2014-10-01
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.http://lod-cloud.net/
RDF Data Volumes . . .
. . . are growing – and fast
Linked data cloud currently consists of 325 datasets with>25B triplesSize almost doubling every year
April ’14:1091 datasets, ???
triples
11Inria/2014-10-01
Max Schmachtenberg, Christian Bizer, and Heiko Paulheim: Adoption of LinkedData Best Practices in Different Topical Domains. In Proc. ISWC, 2014.
Closer Look
12Inria/2014-10-01
Globally Distributed Network of Data
13Inria/2014-10-01
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
14Inria/2014-10-01
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
15Inria/2014-10-01
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
16Inria/2014-10-01
Traditional Hypertext-based Web Access
IMDb WorldBook
Data exposedto the Webvia HTML
17Inria/2014-10-01
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
18Inria/2014-10-01
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
21Inria/2014-10-01
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
“Jack Nicholson”
movie:actor name
mdb:film/3418
“The Passenger”
refs:label
mdb:film/1267
“The Last Tycoon”
refs:label
mdb:director/8476
“Stanley Kubrick”
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
22Inria/2014-10-01
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.
23Inria/2014-10-01
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).23Inria/2014-10-01
SPARQL Queries
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 )
}
?m ?dmovie:director
?name
rdfs:label
?b
movie:relatedBook
“Stanley Kubrick”
movie:director name
?rrev:rating
FILTER(?r > 4.0)
25Inria/2014-10-01
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
26Inria/2014-10-01
Naıve Triple Store Design
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 )
}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!
27Inria/2014-10-01
Naıve Triple Store Design
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 )
}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!
27Inria/2014-10-01
Naıve Triple Store Design
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 )
}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!
27Inria/2014-10-01
Existing Solutions
1. Exhaustive indexing
Create indexes for each permutation of the three columnsQuery components become range queries over individualrelations with merge-join to combineExcessive space usage
2. Property table
Each class of objects go to a different table ⇒ similar tonormalized relationsEliminates some of the joins
3. Binary (vertically partitioned) tables
For each property, build a two-column table, containing bothsubject and object, ordered by subjectsCan use merge join (faster)Good for subject-subject joins but does not help withsubject-object joins
28Inria/2014-10-01
Graph-based Approach
Answering SPARQL query ≡ subgraph matching
gStore [Zou et al., 2011, 2014], chameleon-db [Aluc et al.,2013]
?m ?dmovie:director
?name
rdfs:label
?b
movie:relatedBook
“Stanley Kubrick”
movie:director name
?rrev:rating
FILTER(?r > 4.0)
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
“Jack Nicholson”
movie:actor name
mdb:film/3418
“The Passenger”
refs:label
mdb:film/1267
“The Last Tycoon”
refs:label
mdb:director/8476
“Stanley Kubrick”
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
SubgraphM
atching
29Inria/2014-10-01
Graph-based Approach
Answering SPARQL query ≡ subgraph matching
gStore [Zou et al., 2011, 2014], chameleon-db [Aluc et al.,2013]
?m ?dmovie:director
?name
rdfs:label
?b
movie:relatedBook
“Stanley Kubrick”
movie:director name
?rrev:rating
FILTER(?r > 4.0)
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
“Jack Nicholson”
movie:actor name
mdb:film/3418
“The Passenger”
refs:label
mdb:film/1267
“The Last Tycoon”
refs:label
mdb:director/8476
“Stanley Kubrick”
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
SubgraphM
atching
Advantages
I Maintains the graph structure
I Full set of queries can be handled
29Inria/2014-10-01
Graph-based Approach
Answering SPARQL query ≡ subgraph matching
gStore [Zou et al., 2011, 2014], chameleon-db [Aluc et al.,2013]
?m ?dmovie:director
?name
rdfs:label
?b
movie:relatedBook
“Stanley Kubrick”
movie:director name
?rrev:rating
FILTER(?r > 4.0)
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
“Jack Nicholson”
movie:actor name
mdb:film/3418
“The Passenger”
refs:label
mdb:film/1267
“The Last Tycoon”
refs:label
mdb:director/8476
“Stanley Kubrick”
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
SubgraphM
atching
Advantages
I Maintains the graph structure
I Full set of queries can be handled
Disadvantages
I Graph pattern matching is expensive
29Inria/2014-10-01
Two Systems
gStore
mdb:film/2014
bm:books/0743424425
mdb:director/8476
mdb:film/424mdb:film/2685
mdb:actor/29804
mdb:film/3418 mdb:film/1267
mdb:actor/30013
movie:ac
tor
moive:director
“Spartacus”moive:director moive:director
“Jack_Nicholson”
“A Clockwork Orange”
rdfs:label
“1980-05-23”
rdfs:label
moive:actor_name
y:hasBudget
y:has_box_office
“22000000#dollar”
movie:relatedBook4.7
rev:rating
bm:offers/0743424425
scam:hasOffer
y:hasBudget y:hasBudget
“21000000#dollar” “26589355#dollar” “12000000#dollar” “60000000#dollar”
y:has_box_office
movie:actor
movie:actor movie:actor
“The Passager” “The last Tycoon”rdfs:label rdfs:label
“Scatman Crothers”
movie:initial_release_datemoive:actor_name
Fig. 2. An RDF graph G
?x
?y
?z
mdb:movierdf:type
moive:director
“*Jack*”moive:actor_name
y:hasBudget
?budget<30000000Desc, top10
movie:actor
SELECT ?x ?y WHERE{ ?x hasBudget ?budget. ?x rdf:type mdb:movie. ?x movie:director ?y. ?y movie:actor_name ?z. FILTER( regex(str(?z),``Jack'') AND (?budget <30000000) )}ORDER BY ?budgetLIMIT 10
Fig. 3. SPARQL and Query Graph Q
a query signature graph Q⇤, the encoding strategy is analogueto encoding RDF graphs.
The online query evaluation process consists of two steps:filtering and joining. First, we generate the candidates for eachquery node using VS⇤-tree. Then, applying a depth-first searchstrategy, we perform the multi-way join over these candidatelists to find the subgraph matches of SPARQL query Q overRDF graph G.
III. Techniques
In this section, we briefly discuss the techniques used ingStore system; full details are given in elsewhere [5], [6]. Ac-cording to our framework in Section II, we solve the SPARQLquery processing by subgraph matching over the signaturegraph. A key issue is that the proposed encoding and pruningstrategies should support, in a uniform manner, di↵erent kindsof data (such as strings and numeric data), and SPARQLqueries with di↵erent operators . We discuss the encoding andpruning methods in Section III-A. Another technical issue isthe index structure, which is discussed in Section III-B. Wealso present some system-oriented optimization, such as indexcaching strategy and multicore-based query optimization inour system.
A. Encoding Techniques
In gSore, answering SPARQL queries is equivalent tofinding subgraph matches of query graph Q over RDF graphG. If vertex v (in query Q) can match vertex u (in RDF graphG), each neighbor vertex and each adjacent edge of v shouldmatch to some neighbor vertex and some adjacent edge of u.Thus, given a vertex u in G, we encode each of its adjacentedge labels and the corresponding neighbor vertex labels into
System Architecture
Offline Online
Storage
Input Input
RDF Parser
RDF Graph Builder
Encoding Module
VS*-tree builder
RDF data
RDF Triples
RDF Graph
Signature Graph
Key-Value Store
VS*-treeStore
SPARQL Parser
SPARQL Query
Encoding Module
VS*-tree
Query Graph
Filter Module
Join Module
Signature Graph
Node Candidate
Results
Fig. 4. System Architecture
bitstrings, denoted as vS ig(u). We encode query Q with thesame encoding method. Consequently, the match between Qand G can be verified by simply checking the match betweencorresponding encoded bitstrings.
Given a vertex u, we encode each of its adjacent edgese(eLabel, nLabel) into a bitstring, where eLabel is the edgelabel and nLabel is the vertex label. This bitstring is callededge signature (i.e., eS ig(e)). It has two parts: eS ig(e).e,eS ig(e).n. The first part eS ig(e).e (M bits) denotes the edgelabel (i.e., eLabel) and the second part eS ig(e).n (N bits)denotes the neighbor vertex label (i.e., nLabel). The code ofvS ig(u) is formed by performing OR operator over all eS ig(e).Figure 5 illustrates the process.
mdb:film/2014
mdb:director/8476
mdb:actor/29804 moive:director
“1980-05-23”
y:hasBudget
“22000000#dollar”
movie:initial_release_date
movie:actor
e1 rdfs:label "The Shining"e2 movie:initial_release_date "1980-05-23"e3 movie:director mdb:director/8476e4 movie:actor mdb:actor/29704e5 movie:actor mdb:actor/30013e6 y:hasDuration 7140.0$#se7 y:hasBudget 22000000$#$e8 y:hasImdb "0081505"rdfs:label"The Shining"
hasDuration
hasDuration
"0081505"
y:hasImdb
eSig.e eSig.ne1 001000010 000010000101000e2 000110000 000000011100000e3 100100000 000010010000001e4 000010010 001001000000001e5 000010010 001001010000000e6 101000000 000001001100000e7 001010000 000010000001001e8 100010000 001000001001000
nSig 101110010 001011011101001
Fig. 5. Encoding Technique
1) Computing eS ig(e).e: Given an RDF repository, let |P|denote the number of di↵erent properties. If |P| is small, weset |eS ig(e).e| = |P|, where |eS ig(e).e| denotes the length ofthe bitstring, and build a 1-to-1 mapping between the propertyand the bit position. If |P| is large, we resort to the hashingtechnique. Let |eS ig(e).e| = M. Using an appropriate hashfunction, we set m out of M bits in eS ig(e).e to be ‘1’.
chameleon-db
Structural Index
...
Vertex Index
Spill Index
Clu
ster
Ind
ex
Sto
rag
eS
yst
em Sto
rag
eA
dvis
or
QueryEngine Plan Generation Evaluation
31Inria/2014-10-01
gStore
12,000 lines of C++ code under Linux(plus code for SPARQL parser)
mdb:film/2014
bm:books/0743424425
mdb:director/8476
mdb:film/424mdb:film/2685
mdb:actor/29804
mdb:film/3418 mdb:film/1267
mdb:actor/30013
movie:ac
tor
moive:director
“Spartacus”moive:director moive:director
“Jack_Nicholson”
“A Clockwork Orange”
rdfs:label
“1980-05-23”
rdfs:label
moive:actor_name
y:hasBudget
y:has_box_office
“22000000#dollar”
movie:relatedBook4.7
rev:rating
bm:offers/0743424425
scam:hasOffer
y:hasBudget y:hasBudget
“21000000#dollar” “26589355#dollar” “12000000#dollar” “60000000#dollar”
y:has_box_office
movie:actor
movie:actor movie:actor
“The Passager” “The last Tycoon”rdfs:label rdfs:label
“Scatman Crothers”
movie:initial_release_datemoive:actor_name
Fig. 2. An RDF graph G
?x
?y
?z
mdb:movierdf:type
moive:director
“*Jack*”moive:actor_name
y:hasBudget
?budget<30000000Desc, top10
movie:actor
SELECT ?x ?y WHERE{ ?x hasBudget ?budget. ?x rdf:type mdb:movie. ?x movie:director ?y. ?y movie:actor_name ?z. FILTER( regex(str(?z),``Jack'') AND (?budget <30000000) )}ORDER BY ?budgetLIMIT 10
Fig. 3. SPARQL and Query Graph Q
a query signature graph Q⇤, the encoding strategy is analogueto encoding RDF graphs.
The online query evaluation process consists of two steps:filtering and joining. First, we generate the candidates for eachquery node using VS⇤-tree. Then, applying a depth-first searchstrategy, we perform the multi-way join over these candidatelists to find the subgraph matches of SPARQL query Q overRDF graph G.
III. Techniques
In this section, we briefly discuss the techniques used ingStore system; full details are given in elsewhere [5], [6]. Ac-cording to our framework in Section II, we solve the SPARQLquery processing by subgraph matching over the signaturegraph. A key issue is that the proposed encoding and pruningstrategies should support, in a uniform manner, di↵erent kindsof data (such as strings and numeric data), and SPARQLqueries with di↵erent operators . We discuss the encoding andpruning methods in Section III-A. Another technical issue isthe index structure, which is discussed in Section III-B. Wealso present some system-oriented optimization, such as indexcaching strategy and multicore-based query optimization inour system.
A. Encoding Techniques
In gSore, answering SPARQL queries is equivalent tofinding subgraph matches of query graph Q over RDF graphG. If vertex v (in query Q) can match vertex u (in RDF graphG), each neighbor vertex and each adjacent edge of v shouldmatch to some neighbor vertex and some adjacent edge of u.Thus, given a vertex u in G, we encode each of its adjacentedge labels and the corresponding neighbor vertex labels into
System Architecture
Offline Online
Storage
Input Input
RDF Parser
RDF Graph Builder
Encoding Module
VS*-tree builder
RDF data
RDF Triples
RDF Graph
Signature Graph
Key-Value Store
VS*-treeStore
SPARQL Parser
SPARQL Query
Encoding Module
VS*-tree
Query Graph
Filter Module
Join Module
Signature Graph
Node Candidate
Results
Fig. 4. System Architecture
bitstrings, denoted as vS ig(u). We encode query Q with thesame encoding method. Consequently, the match between Qand G can be verified by simply checking the match betweencorresponding encoded bitstrings.
Given a vertex u, we encode each of its adjacent edgese(eLabel, nLabel) into a bitstring, where eLabel is the edgelabel and nLabel is the vertex label. This bitstring is callededge signature (i.e., eS ig(e)). It has two parts: eS ig(e).e,eS ig(e).n. The first part eS ig(e).e (M bits) denotes the edgelabel (i.e., eLabel) and the second part eS ig(e).n (N bits)denotes the neighbor vertex label (i.e., nLabel). The code ofvS ig(u) is formed by performing OR operator over all eS ig(e).Figure 5 illustrates the process.
mdb:film/2014
mdb:director/8476
mdb:actor/29804 moive:director
“1980-05-23”
y:hasBudget
“22000000#dollar”
movie:initial_release_date
movie:actor
e1 rdfs:label "The Shining"e2 movie:initial_release_date "1980-05-23"e3 movie:director mdb:director/8476e4 movie:actor mdb:actor/29704e5 movie:actor mdb:actor/30013e6 y:hasDuration 7140.0$#se7 y:hasBudget 22000000$#$e8 y:hasImdb "0081505"rdfs:label"The Shining"
hasDuration
hasDuration
"0081505"
y:hasImdb
eSig.e eSig.ne1 001000010 000010000101000e2 000110000 000000011100000e3 100100000 000010010000001e4 000010010 001001000000001e5 000010010 001001010000000e6 101000000 000001001100000e7 001010000 000010000001001e8 100010000 001000001001000
nSig 101110010 001011011101001
Fig. 5. Encoding Technique
1) Computing eS ig(e).e: Given an RDF repository, let |P|denote the number of di↵erent properties. If |P| is small, weset |eS ig(e).e| = |P|, where |eS ig(e).e| denotes the length ofthe bitstring, and build a 1-to-1 mapping between the propertyand the bit position. If |P| is large, we resort to the hashingtechnique. Let |eS ig(e).e| = M. Using an appropriate hashfunction, we set m out of M bits in eS ig(e).e to be ‘1’.
General Approach:
Work directly on the RDF graphand the SPARQL query graph
Use a signature-based encoding ofeach entity and class vertex tospeed up matching
Filter-and-evaluate
Use a false positive algorithmto prune nodes and obtain aset of candidates; then domore detailed evaluation onthose
Use an index (VS∗-tree) over thedata signature graph (has lightmaintenance load) for efficientpruning
32Inria/2014-10-01
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
33Inria/2014-10-01
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
34Inria/2014-10-01
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
35Inria/2014-10-01
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
35Inria/2014-10-01
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
35Inria/2014-10-01
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
35Inria/2014-10-01
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
35Inria/2014-10-01
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!
36Inria/2014-10-01
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!
36Inria/2014-10-01
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
Possibly large join space!
36Inria/2014-10-01
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
Possibly large join space!
36Inria/2014-10-01
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
Possibly large join space!
36Inria/2014-10-01
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
Possibly large join space!
36Inria/2014-10-01
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
Possibly large join space!
36Inria/2014-10-01
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
37Inria/2014-10-01
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!
38Inria/2014-10-01
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!
38Inria/2014-10-01
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!
38Inria/2014-10-01
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!
38Inria/2014-10-01
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!
38Inria/2014-10-01
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!
38Inria/2014-10-01
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., 2014a]
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
39Inria/2014-10-01
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., 2014a]
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
39Inria/2014-10-01
Group-by-Query Approach
Tamer Post23571hasPost
OlaftaggedIn
UWaterlooworksAt
Tamer Post23hasPost
Boblikes
UWaterlooworksAt
Post2hasPost taggedIn
Tamer Post23hasPost
BobtaggedIn
UWaterlooworksAt
Post2hasPost favourites
40Inria/2014-10-01
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
41Inria/2014-10-01
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
Storage System
CacheHash
Function
evict
@t1
· · ·
functionadapts
HashFunction
@tk
41Inria/2014-10-01
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
Storage System
CacheHash
Function
evict
@t1
· · ·
functionadapts
HashFunction
@tk
Index – – – – – – – – – –
SPARQL Query Engine
41Inria/2014-10-01
chameleon-dbPrototype system [Aluc et al., 2013]35,000 lines of code in C++ under Linux (plus code forSPARQL 1.0 parser)
Structural Index
...
Vertex Index
Spill Index
Clu
ster
Inde
xS
tora
geS
yste
m Sto
rage
Adv
isor
QueryEngine Plan Generation Evaluation
42Inria/2014-10-01
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
45Inria/2014-10-01
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)
46Inria/2014-10-01
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)
46Inria/2014-10-01
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 decomposition
SPARQL federation: justprocess at SPARQLendpointsLive querying (see nextsection)
46Inria/2014-10-01
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 SPARQLendpoints
Live querying (see nextsection)
46Inria/2014-10-01
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)
46Inria/2014-10-01
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)
47Inria/2014-10-01
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)
47Inria/2014-10-01
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)
47Inria/2014-10-01
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)
47Inria/2014-10-01
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
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]
49Inria/2014-10-01
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
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]
49Inria/2014-10-01
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
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]
49Inria/2014-10-01
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
50Inria/2014-10-01
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
50Inria/2014-10-01
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
52Inria/2014-10-01
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
53Inria/2014-10-01
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
54Inria/2014-10-01
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
54Inria/2014-10-01
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
55Inria/2014-10-01
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
Advantages
Easy to implementNo data structure to maintain
55Inria/2014-10-01
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
Advantages
Easy to implementNo data structure to maintain
Disadvantages
Possibilities for parallelized data retrieval are limited (can do asmuch as parallel crawling)Repeated data retrieval introduces significant query latency
55Inria/2014-10-01
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
57Inria/2014-10-01
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
Advantages
Data retrieval can be fully parallelizedReduces the impact of data retrieval on query execution time
57Inria/2014-10-01
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
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
57Inria/2014-10-01
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
58Inria/2014-10-01
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
60Inria/2014-10-01
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
61Inria/2014-10-01
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
61Inria/2014-10-01
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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