SlideShare a Scribd company logo
Querying datasets on the Web 
with high availability 
Ruben Verborgh, Olaf Hartig, Ben De Meester, Gerald Haesendonck, 
Laurens De Vocht, Miel Vander Sande, Richard Cyganiak, Pieter Colpaert, 
Erik Mannens, and Rik Van de Walle
Somebody comes to you 
with a high-quality 
Linked Open Data set: 
“How can people query 
my Linked Data on the Web?”
“A public SPARQL endpoint 
gives live querying, but it’s costly 
and has availability issues.” 
“Offer a data dump. 
but it’s not really Web querying: 
users need to set up an endpoint” 
“Publish Linked Data documents. 
But querying is very slow…”
Querying Linked Data 
on the Web 
always involves trade-offs. 
But have we looked 
at all possible trade-offs?
Querying Linked Data 
live on the Web 
becomes affordable 
by building simpler servers 
and more intelligent clients.
We formalized and evaluated 
client-side SPARQL querying 
through a triple-pattern interface. 
The trade-offs are different: 
low-cost, high-availability servers, 
more bandwidth and slower queries.
Querying datasets on the Web 
with high availability 
Trade-offs for the Semantic Web 
Triple pattern fragment querying 
Evaluating the trade-offs
Querying datasets on the Web 
with high availability 
Trade-offs for the Semantic Web 
Triple pattern fragment querying 
Evaluating the trade-offs
It is not an all-or-nothing world. 
There is a spectrum of trade-offs. 
out-of-date data live data 
high bandwidth low bandwidth 
high availability low availability 
high client cost low client cost 
low server cost high server cost 
data 
dump 
SPARQL 
endpoint 
Linked Data 
documents 
interface offered by the server
Linked Data Fragments are 
a uniform view on Linked Data interfaces. 
data 
dump 
SPARQL 
endpoint 
Every Linked Data interface 
offers specific fragments 
of a Linked Data set. 
Linked Data 
documents 
interface offered by the server
Each type of Linked Data Fragment 
is defined by three characteristics. 
data 
metadata 
controls 
What triples does it contain? 
What do we know about it? 
How to access more data?
Each type of Linked Data Fragment 
is defined by three characteristics. 
all dataset triples 
(none) 
data dump 
number of triples, file size 
data 
metadata 
controls
Each type of Linked Data Fragment 
is defined by three characteristics. 
SPARQL query result 
data 
triples matching the query 
(none) 
(none) 
metadata 
controls
Each type of Linked Data Fragment 
is defined by three characteristics. 
Linked Data Document 
data 
triples about a subject 
creator, maintainer, … 
links to other LD documents 
metadata 
controls
We designed a new trade-off mix 
with low cost and high availability. 
out-of-date data live data 
high bandwidth low bandwidth 
high availability low availability 
high client cost low client cost 
low server cost high server cost 
data 
dump 
SPARQL 
query results 
Linked Data 
documents
A triple pattern fragments interface 
is low-cost and enables clients to query. 
live data 
low server cost 
data 
dump 
SPARQL 
query results 
high availability 
Linked Data 
documents 
triple pattern 
fragments
A triple pattern fragments interface 
is low-cost and enables clients to query. 
matches of a triple pattern 
total number of matches 
access to all other fragments 
data 
metadata 
controls 
(paged)
controls (other fragments) 
metadata (total count) 
data (first 100)
Triple patterns are not the final answer. 
No interface ever will be. 
Triple patterns show how far we can get 
with simple servers and smart clients. 
data 
dump 
SPARQL 
query results 
Linked Data 
documents 
triple pattern 
fragments
Querying datasets on the Web 
with high availability 
Trade-offs for the Semantic Web 
Triple pattern fragment querying 
Evaluating the trade-offs
Triple pattern fragment servers 
enable clients to be intelligent. 
The HTML representation explains: 
“you can query by triple pattern”. 
controls
Triple pattern fragment servers 
enable clients to be intelligent. 
<http://fragments.dbpedia.org/2014/en#dataset> hydra:search [ 
hydra:template "http://fragments.dbpedia.org/2014/en 
controls 
{?subject,predicate,object}"; 
hydra:mapping 
[ hydra:variable "subject"; hydra:property rdf:subject ], 
[ hydra:variable "predicate"; hydra:property rdf:predicate ], 
[ hydra:variable "object"; hydra:property rdf:object ] 
]. 
The RDF representation explains: 
“you can query by triple pattern”.
Triple pattern fragment servers 
enable clients to be intelligent. 
The HTML representation explains: 
“this is the number of matches”. 
metadata
Triple pattern fragment servers 
enable clients to be intelligent. 
<#fragment> void:triples 8141. 
The RDF representation explains: 
“this is the number of matches”. 
metadata
How can intelligent clients 
solve SPARQL queries over fragments? 
Give them a SPARQL query. 
Give them a URL of any dataset fragment. 
They look inside the fragment 
to see how to access the dataset 
and use the metadata 
to decide how to plan the query.
Let’s follow the execution 
of an example SPARQL query. 
Find names of artists born in Padua, Italy. 
SELECT ?artist ?name WHERE { 
?artist a dbpedia-owl:Artist; 
rdfs:label ?name; 
dbpedia-owl:birthPlace dbpedia:Padua. 
FILTER LANGMATCHES(LANG(?name), "EN") 
} 
Fragment: http://fragments.dbpedia.org/2014/en
The client looks inside the fragment 
to see how to access the dataset. 
Fragment: http://fragments.dbpedia.org/2014/en 
<http://fragments.dbpedia.org/2014/en#dataset> hydra:search [ 
hydra:template "http://fragments.dbpedia.org/2014/en 
{?subject,predicate,object}"; 
hydra:mapping 
[ hydra:variable "subject"; hydra:property rdf:subject ], 
[ hydra:variable "predicate"; hydra:property rdf:predicate ], 
[ hydra:variable "object"; hydra:property rdf:object ] 
]. 
“I can query the dataset by triple pattern.”
The client splits the query 
into the available fragments. 
SELECT ?artist ?name WHERE { 
?artist a dbpedia-owl:Artist; 
rdfs:label ?name; 
dbpedia-owl:birthPlace dbpedia:Padua. 
FILTER LANGMATCHES(LANG(?name), "EN") 
}
The client gets the fragments 
and inspects their metadata. 
?artist a dbpedia-owl:Artist. 
first 100 triples 
96.000 
?artist rdfs:label ?name. 
first 100 triples 
12.000.000 
?artist dbont:birthPlace dbpedia:Padua. 
first 100 triples 
135
The metadata enables the client 
to choose the right starting point. 
?dbp:artist Alberto_a dbpedia-Benettin owl:a Artist. dbont:Artist. 
96.000 
?artist rdfs:label ?name. 12.000.000 
dbp:Alberto_Benettin rdfs:label ?name. 
?artist dbont:birthPlace dbpedia:Padua. 
135 
dbpedia:Alberto_Benettin dbont:birthPlace dbpedia:Padua. 
dbpedia:Alberto_Bigon dbont:birthPlace dbpedia:Padua.
Clients execute the query in 3 seconds 
on a highly available, low-cost server. 
SELECT ?artist ?name WHERE { 
?artist a dbpedia-owl:Artist; 
rdfs:label ?name; 
dbpedia-owl:birthPlace dbpedia:Padua. 
FILTER LANGMATCHES(LANG(?name), "EN") 
} 
Try it yourself: 
bit.ly/artistspadua
Querying datasets on the Web 
with high availability 
Trade-offs for the Semantic Web 
Triple pattern fragment querying 
Evaluating the trade-offs
We evaluated triple pattern fragments 
for server cost and availability. 
Berlin SPARQL benchmark 
on Amazon EC2 machines 
100 million triples 
high query diversity 
BGP, UNION, FILTER, …
We evaluated triple pattern fragments 
for server cost and availability. 
Berlin SPARQL benchmark 
on Amazon EC2 machines 
1 server 
1 cache 
1–240 simultaneous clients
The query throughput is lower, 
but resilient to high client numbers. 
Querying Datasets on 1 10 100 10 100 1000 10000 clients 
throughput (q/hr) 
Virtuoso 6 Fuseki–tdb triple pattern Fig. 3.1: Server performance (log-log plot) 
200 
executed SPARQL queries per hour 
timeouts
The server traffic is higher, 
but requests are significantly lighter. 
Datasets on the Web with High Availability 13 
Virtuoso 6 Virtuoso 7 
Fuseki–tdb Fuseki–hdt 
pattern fragments 
1 10 100 
4 
2 
0 
clients 
data sent (mb) 
Fig. 3.2: Server network traffic 
data sent by server in MB
2 
Caching is significantly more effective, 
as clients reuse fragments for queries. 
1 10 100 
0 
clients 
sent (mb) 
Fig. 3.2: Server network traffic 
1 10 100 
20 
10 
0 
clients 
sent (mb) 
Fig. 3.4: Cache network traffic 
ram use data sent by cache in MB 
8 
6 
4
150 
The server uses much less CPU, 
allowing for higher availability. 
clients 
1 10 100 
1 1 10 100 
100 
50 
0 
100 
50 
1 50 
server CPU usage per core 
#timeouts 
Fig. 3.3: Query timeouts 
0 
clients 
cpu use (%) 
Fig. 3.5: Server processor usage per core 
100 
use (%)
150 
Servers enable clients to be intelligent, 
so they remain simple and light-weight. 
1 10 100 
1 1 10 100 
100 
50 
0 
clients 
#timeouts 
Fig. 3.3: Query timeouts 
100 
50 
0 
clients 
cpu use (%) 
1 Fig. 3.5: Server processor usage per core 
50 
100 
use (%) 
server CPU usage per core
Querying datasets on the Web 
with high availability 
Trade-offs for the Semantic Web 
Triple pattern fragment querying 
Evaluating the trade-offs
Triple pattern fragments allow 
querying live data with high availability. 
live data 
low server cost 
data 
dump 
SPARQL 
query results 
high availability 
Linked Data 
documents 
triple pattern 
fragments
Like each Linked Data query method, 
triple pattern fragments have trade-offs. 
Live data 
Low-cost and highly available servers 
More bandwidth 
Slower results (but they are streaming)
Experience the trade-offs yourself 
on the official DBpedia interfaces. 
DBpedia data dump 
DBpedia Linked Data documents 
DBpedia SPARQL endpoint 
DBpedia triple pattern fragments 
fragments.dbpedia.org
Triple pattern fragments are easy: 
all software is available as open source. 
Software 
github.com/LinkedDataFragments 
Documentation and specification 
linkeddatafragments.org
Querying is a client–server dialogue. 
Start exploring the entire spectrum. 
data 
dump 
SPARQL 
query results 
Linked Data 
documents 
triple pattern 
fragments
Querying datasets on the Web 
with high availability 
linkeddatafragments.org 
@LDFragments

More Related Content

What's hot

The Digital Cavemen of Linked Lascaux
The Digital Cavemen of Linked LascauxThe Digital Cavemen of Linked Lascaux
The Digital Cavemen of Linked Lascaux
Ruben Verborgh
 
Functional Composition of Sensor Web APIs
Functional Composition of Sensor Web APIsFunctional Composition of Sensor Web APIs
Functional Composition of Sensor Web APIs
Ruben Verborgh
 
The Lonesome LOD Cloud
The Lonesome LOD CloudThe Lonesome LOD Cloud
The Lonesome LOD Cloud
Ruben Verborgh
 
Reasoned SPARQL
Reasoned SPARQLReasoned SPARQL
Reasoned SPARQL
Ruben Verborgh
 
Distributed Affordance
Distributed AffordanceDistributed Affordance
Distributed Affordance
Ruben Verborgh
 
Linking media, data, and services
Linking media, data, and servicesLinking media, data, and services
Linking media, data, and services
Ruben Verborgh
 
The web – A hypermedia story
The web – A hypermedia storyThe web – A hypermedia story
The web – A hypermedia story
Ruben Verborgh
 
Hypermedia APIs that make sense
Hypermedia APIs that make senseHypermedia APIs that make sense
Hypermedia APIs that make sense
Ruben Verborgh
 
Hypermedia Cannot be the Engine
Hypermedia Cannot be the EngineHypermedia Cannot be the Engine
Hypermedia Cannot be the Engine
Ruben Verborgh
 
Creating 3rd Generation Web APIs with Hydra
Creating 3rd Generation Web APIs with HydraCreating 3rd Generation Web APIs with Hydra
Creating 3rd Generation Web APIs with Hydra
Markus Lanthaler
 
Overview of GraphQL & Clients
Overview of GraphQL & ClientsOverview of GraphQL & Clients
Overview of GraphQL & Clients
Pokai Chang
 
RESTdesc – Efficient runtime service discovery and consumption
RESTdesc – Efficient runtime service discovery and consumptionRESTdesc – Efficient runtime service discovery and consumption
RESTdesc – Efficient runtime service discovery and consumption
Ruben Verborgh
 
CrossRef Technical Information for Libraries
CrossRef Technical Information for LibrariesCrossRef Technical Information for Libraries
CrossRef Technical Information for Libraries
Crossref
 
Synchronicity: Just-In-Time Discovery of Lost Web Pages
Synchronicity: Just-In-Time Discovery of Lost Web PagesSynchronicity: Just-In-Time Discovery of Lost Web Pages
Synchronicity: Just-In-Time Discovery of Lost Web Pages
Michael Nelson
 
On the Persistence of Persistent Identifiers of the Scholarly Web
On the Persistence of Persistent Identifiers of the Scholarly WebOn the Persistence of Persistent Identifiers of the Scholarly Web
On the Persistence of Persistent Identifiers of the Scholarly Web
Martin Klein
 
Opportunistic Linked Data Querying through Approximate Membership Metadata
Opportunistic Linked Data Querying through Approximate Membership MetadataOpportunistic Linked Data Querying through Approximate Membership Metadata
Opportunistic Linked Data Querying through Approximate Membership Metadata
Miel Vander Sande
 
STACK OVERFLOW DATASET ANALYSIS
STACK OVERFLOW DATASET ANALYSISSTACK OVERFLOW DATASET ANALYSIS
STACK OVERFLOW DATASET ANALYSIS
Shrinivasaragav Balasubramanian
 
GraphQL & Relay - 串起前後端世界的橋樑
GraphQL & Relay - 串起前後端世界的橋樑GraphQL & Relay - 串起前後端世界的橋樑
GraphQL & Relay - 串起前後端世界的橋樑
Pokai Chang
 
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAPOpen Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
Pieter De Leenheer
 
Log File Analysis: The most powerful tool in your SEO toolkit
Log File Analysis: The most powerful tool in your SEO toolkitLog File Analysis: The most powerful tool in your SEO toolkit
Log File Analysis: The most powerful tool in your SEO toolkit
Tom Bennet
 

What's hot (20)

The Digital Cavemen of Linked Lascaux
The Digital Cavemen of Linked LascauxThe Digital Cavemen of Linked Lascaux
The Digital Cavemen of Linked Lascaux
 
Functional Composition of Sensor Web APIs
Functional Composition of Sensor Web APIsFunctional Composition of Sensor Web APIs
Functional Composition of Sensor Web APIs
 
The Lonesome LOD Cloud
The Lonesome LOD CloudThe Lonesome LOD Cloud
The Lonesome LOD Cloud
 
Reasoned SPARQL
Reasoned SPARQLReasoned SPARQL
Reasoned SPARQL
 
Distributed Affordance
Distributed AffordanceDistributed Affordance
Distributed Affordance
 
Linking media, data, and services
Linking media, data, and servicesLinking media, data, and services
Linking media, data, and services
 
The web – A hypermedia story
The web – A hypermedia storyThe web – A hypermedia story
The web – A hypermedia story
 
Hypermedia APIs that make sense
Hypermedia APIs that make senseHypermedia APIs that make sense
Hypermedia APIs that make sense
 
Hypermedia Cannot be the Engine
Hypermedia Cannot be the EngineHypermedia Cannot be the Engine
Hypermedia Cannot be the Engine
 
Creating 3rd Generation Web APIs with Hydra
Creating 3rd Generation Web APIs with HydraCreating 3rd Generation Web APIs with Hydra
Creating 3rd Generation Web APIs with Hydra
 
Overview of GraphQL & Clients
Overview of GraphQL & ClientsOverview of GraphQL & Clients
Overview of GraphQL & Clients
 
RESTdesc – Efficient runtime service discovery and consumption
RESTdesc – Efficient runtime service discovery and consumptionRESTdesc – Efficient runtime service discovery and consumption
RESTdesc – Efficient runtime service discovery and consumption
 
CrossRef Technical Information for Libraries
CrossRef Technical Information for LibrariesCrossRef Technical Information for Libraries
CrossRef Technical Information for Libraries
 
Synchronicity: Just-In-Time Discovery of Lost Web Pages
Synchronicity: Just-In-Time Discovery of Lost Web PagesSynchronicity: Just-In-Time Discovery of Lost Web Pages
Synchronicity: Just-In-Time Discovery of Lost Web Pages
 
On the Persistence of Persistent Identifiers of the Scholarly Web
On the Persistence of Persistent Identifiers of the Scholarly WebOn the Persistence of Persistent Identifiers of the Scholarly Web
On the Persistence of Persistent Identifiers of the Scholarly Web
 
Opportunistic Linked Data Querying through Approximate Membership Metadata
Opportunistic Linked Data Querying through Approximate Membership MetadataOpportunistic Linked Data Querying through Approximate Membership Metadata
Opportunistic Linked Data Querying through Approximate Membership Metadata
 
STACK OVERFLOW DATASET ANALYSIS
STACK OVERFLOW DATASET ANALYSISSTACK OVERFLOW DATASET ANALYSIS
STACK OVERFLOW DATASET ANALYSIS
 
GraphQL & Relay - 串起前後端世界的橋樑
GraphQL & Relay - 串起前後端世界的橋樑GraphQL & Relay - 串起前後端世界的橋樑
GraphQL & Relay - 串起前後端世界的橋樑
 
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAPOpen Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
Open Standards for the Semantic Web: XML / RDF(S) / OWL / SOAP
 
Log File Analysis: The most powerful tool in your SEO toolkit
Log File Analysis: The most powerful tool in your SEO toolkitLog File Analysis: The most powerful tool in your SEO toolkit
Log File Analysis: The most powerful tool in your SEO toolkit
 

Similar to Querying datasets on the Web with high availability

Time travelling through DBpedia
Time travelling through DBpediaTime travelling through DBpedia
Time travelling through DBpedia
Miel Vander Sande
 
Deep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDBDeep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDB
ArangoDB Database
 
A sweet affordable combo for Linked Data Archives
A sweet affordable combo for Linked Data ArchivesA sweet affordable combo for Linked Data Archives
A sweet affordable combo for Linked Data Archives
Miel Vander Sande
 
Reproducibility with 
the 99 cents Linked Data archive
Reproducibility with 
the 99 cents Linked Data archiveReproducibility with 
the 99 cents Linked Data archive
Reproducibility with 
the 99 cents Linked Data archive
Miel Vander Sande
 
Hatkit Project - Datafiddler
Hatkit Project - DatafiddlerHatkit Project - Datafiddler
Hatkit Project - Datafiddler
holiman
 
How to govern and secure a Data Mesh?
How to govern and secure a Data Mesh?How to govern and secure a Data Mesh?
How to govern and secure a Data Mesh?
confluent
 
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
confluent
 
What is a distributed data science pipeline. how with apache spark and friends.
What is a distributed data science pipeline. how with apache spark and friends.What is a distributed data science pipeline. how with apache spark and friends.
What is a distributed data science pipeline. how with apache spark and friends.
Andy Petrella
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
Giorgos Santipantakis
 
Data Science with the Help of Metadata
Data Science with the Help of MetadataData Science with the Help of Metadata
Data Science with the Help of Metadata
Jim Dowling
 
Databricks Platform.pptx
Databricks Platform.pptxDatabricks Platform.pptx
Databricks Platform.pptx
Alex Ivy
 
Document Databases & RavenDB
Document Databases & RavenDBDocument Databases & RavenDB
Document Databases & RavenDB
Brian Ritchie
 
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
Amazon Web Services
 
Data Architectures for Robust Decision Making
Data Architectures for Robust Decision MakingData Architectures for Robust Decision Making
Data Architectures for Robust Decision Making
Gwen (Chen) Shapira
 
Big Data, Mob Scale.
Big Data, Mob Scale.Big Data, Mob Scale.
Big Data, Mob Scale.
darach
 
Big Events, Mob Scale - Darach Ennis (Push Technology)
Big Events, Mob Scale - Darach Ennis (Push Technology)Big Events, Mob Scale - Darach Ennis (Push Technology)
Big Events, Mob Scale - Darach Ennis (Push Technology)
jaxLondonConference
 
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
National Information Standards Organization (NISO)
 
Computing Outside The Box June 2009
Computing Outside The Box June 2009Computing Outside The Box June 2009
Computing Outside The Box June 2009
Ian Foster
 
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Amazon Web Services
 
Practical OData
Practical ODataPractical OData
Practical OData
Vagif Abilov
 

Similar to Querying datasets on the Web with high availability (20)

Time travelling through DBpedia
Time travelling through DBpediaTime travelling through DBpedia
Time travelling through DBpedia
 
Deep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDBDeep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDB
 
A sweet affordable combo for Linked Data Archives
A sweet affordable combo for Linked Data ArchivesA sweet affordable combo for Linked Data Archives
A sweet affordable combo for Linked Data Archives
 
Reproducibility with 
the 99 cents Linked Data archive
Reproducibility with 
the 99 cents Linked Data archiveReproducibility with 
the 99 cents Linked Data archive
Reproducibility with 
the 99 cents Linked Data archive
 
Hatkit Project - Datafiddler
Hatkit Project - DatafiddlerHatkit Project - Datafiddler
Hatkit Project - Datafiddler
 
How to govern and secure a Data Mesh?
How to govern and secure a Data Mesh?How to govern and secure a Data Mesh?
How to govern and secure a Data Mesh?
 
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...
 
What is a distributed data science pipeline. how with apache spark and friends.
What is a distributed data science pipeline. how with apache spark and friends.What is a distributed data science pipeline. how with apache spark and friends.
What is a distributed data science pipeline. how with apache spark and friends.
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
 
Data Science with the Help of Metadata
Data Science with the Help of MetadataData Science with the Help of Metadata
Data Science with the Help of Metadata
 
Databricks Platform.pptx
Databricks Platform.pptxDatabricks Platform.pptx
Databricks Platform.pptx
 
Document Databases & RavenDB
Document Databases & RavenDBDocument Databases & RavenDB
Document Databases & RavenDB
 
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...
 
Data Architectures for Robust Decision Making
Data Architectures for Robust Decision MakingData Architectures for Robust Decision Making
Data Architectures for Robust Decision Making
 
Big Data, Mob Scale.
Big Data, Mob Scale.Big Data, Mob Scale.
Big Data, Mob Scale.
 
Big Events, Mob Scale - Darach Ennis (Push Technology)
Big Events, Mob Scale - Darach Ennis (Push Technology)Big Events, Mob Scale - Darach Ennis (Push Technology)
Big Events, Mob Scale - Darach Ennis (Push Technology)
 
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
Chachra, "Improving Discovery Systems Through Post Processing of Harvested Data"
 
Computing Outside The Box June 2009
Computing Outside The Box June 2009Computing Outside The Box June 2009
Computing Outside The Box June 2009
 
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
 
Practical OData
Practical ODataPractical OData
Practical OData
 

Recently uploaded

Book dating , international dating phgra
Book dating , international dating phgraBook dating , international dating phgra
Book dating , international dating phgra
thomaskurtha9
 
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirtsJarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
exgf28
 
workbook and project U5 1ºsecundaria.pdf
workbook and project U5 1ºsecundaria.pdfworkbook and project U5 1ºsecundaria.pdf
workbook and project U5 1ºsecundaria.pdf
anya2024forgya
 
UMN degree offer diploma Transcript
UMN degree offer diploma TranscriptUMN degree offer diploma Transcript
UMN degree offer diploma Transcript
cenocb
 
Effective Tips for Creating the Best Rich Media Ads .pptx
Effective Tips for Creating the Best Rich Media Ads .pptxEffective Tips for Creating the Best Rich Media Ads .pptx
Effective Tips for Creating the Best Rich Media Ads .pptx
AirtoryInc
 
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
shamrisumri
 
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5 hot nhất
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5  hot nhấtBai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5  hot nhất
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5 hot nhất
Thiên Đường Tình Yêu
 
Megalive99 Situs Betting Online Gacor Terpercaya
Megalive99 Situs Betting Online Gacor TerpercayaMegalive99 Situs Betting Online Gacor Terpercaya
Megalive99 Situs Betting Online Gacor Terpercaya
Megalive99
 
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptxDraya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
ashishkumarrana9
 
AWS Networking Basic , tanapat limsaiprom
AWS Networking Basic , tanapat limsaipromAWS Networking Basic , tanapat limsaiprom
AWS Networking Basic , tanapat limsaiprom
ธนาพัฒน์ ลิ้มสายพรหม
 
Information Systems Auditing, Controls and Assurance , tanapat limsaiprom
Information Systems Auditing, Controls and Assurance , tanapat limsaipromInformation Systems Auditing, Controls and Assurance , tanapat limsaiprom
Information Systems Auditing, Controls and Assurance , tanapat limsaiprom
TanapatLimsaiprom1
 
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
ffg01100
 
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
ffg01100
 
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai AvailableChennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
shamrisumri
 
6 Reasons to Use a VPN | 3S VPN Server App
6 Reasons to Use a VPN | 3S VPN Server App6 Reasons to Use a VPN | 3S VPN Server App
6 Reasons to Use a VPN | 3S VPN Server App
VPN Server
 
Top 50 Telephone Conversation Sample Examples For IT Industries.pdf
Top 50 Telephone Conversation Sample Examples For IT Industries.pdfTop 50 Telephone Conversation Sample Examples For IT Industries.pdf
Top 50 Telephone Conversation Sample Examples For IT Industries.pdf
Krishna L
 
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdfHow-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
Dolphin Data Lab
 
Trading Strategy for London silver bullet
Trading Strategy for London silver bulletTrading Strategy for London silver bullet
Trading Strategy for London silver bullet
OkgatoSemadi1
 
Tarun Gaur On Data Breaches and Privacy Fears
Tarun Gaur On Data Breaches and Privacy FearsTarun Gaur On Data Breaches and Privacy Fears
Tarun Gaur On Data Breaches and Privacy Fears
Tarun Gaur
 
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
shamrisumri
 

Recently uploaded (20)

Book dating , international dating phgra
Book dating , international dating phgraBook dating , international dating phgra
Book dating , international dating phgra
 
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirtsJarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
Jarren Duran Fuck EM T shirts Jarren Duran Fuck EM T shirts
 
workbook and project U5 1ºsecundaria.pdf
workbook and project U5 1ºsecundaria.pdfworkbook and project U5 1ºsecundaria.pdf
workbook and project U5 1ºsecundaria.pdf
 
UMN degree offer diploma Transcript
UMN degree offer diploma TranscriptUMN degree offer diploma Transcript
UMN degree offer diploma Transcript
 
Effective Tips for Creating the Best Rich Media Ads .pptx
Effective Tips for Creating the Best Rich Media Ads .pptxEffective Tips for Creating the Best Rich Media Ads .pptx
Effective Tips for Creating the Best Rich Media Ads .pptx
 
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
High Profile Girls Call ServiCe Chennai XX00XXX00X Tanisha Best High Class Ch...
 
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5 hot nhất
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5  hot nhấtBai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5  hot nhất
Bai-Tập-Tiếng-Anh-On-Tập-He lớp 1- lớp 5 hot nhất
 
Megalive99 Situs Betting Online Gacor Terpercaya
Megalive99 Situs Betting Online Gacor TerpercayaMegalive99 Situs Betting Online Gacor Terpercaya
Megalive99 Situs Betting Online Gacor Terpercaya
 
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptxDraya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
Draya Michele’s Son – Kniko Howard’s Rise to Fame.pptx
 
AWS Networking Basic , tanapat limsaiprom
AWS Networking Basic , tanapat limsaipromAWS Networking Basic , tanapat limsaiprom
AWS Networking Basic , tanapat limsaiprom
 
Information Systems Auditing, Controls and Assurance , tanapat limsaiprom
Information Systems Auditing, Controls and Assurance , tanapat limsaipromInformation Systems Auditing, Controls and Assurance , tanapat limsaiprom
Information Systems Auditing, Controls and Assurance , tanapat limsaiprom
 
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
202254.com免费观看《长相思第二季》免费观看高清,长相思第二季线上看,《长相思第二季》最新电视剧在线观看,杨紫最新电视剧
 
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
202254.com香蕉影视,在线观看《我才不要和你做朋友呢》在线观看最新电影,香蕉影视在线观看《我才不要和你做朋友呢》在线观看高清电影
 
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai AvailableChennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
Chennai Girls Call ServiCe X00XXX00XX Tanisha Best High Class Chennai Available
 
6 Reasons to Use a VPN | 3S VPN Server App
6 Reasons to Use a VPN | 3S VPN Server App6 Reasons to Use a VPN | 3S VPN Server App
6 Reasons to Use a VPN | 3S VPN Server App
 
Top 50 Telephone Conversation Sample Examples For IT Industries.pdf
Top 50 Telephone Conversation Sample Examples For IT Industries.pdfTop 50 Telephone Conversation Sample Examples For IT Industries.pdf
Top 50 Telephone Conversation Sample Examples For IT Industries.pdf
 
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdfHow-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
How-to-Diagnose-Hard-Drives-by-DFL-DDP-2024.pdf
 
Trading Strategy for London silver bullet
Trading Strategy for London silver bulletTrading Strategy for London silver bullet
Trading Strategy for London silver bullet
 
Tarun Gaur On Data Breaches and Privacy Fears
Tarun Gaur On Data Breaches and Privacy FearsTarun Gaur On Data Breaches and Privacy Fears
Tarun Gaur On Data Breaches and Privacy Fears
 
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
@Girls @Call Chennai 🛬 XXXXXXXXXX 🛬 available 24*7 cash payment book now pay ...
 

Querying datasets on the Web with high availability

  • 1. Querying datasets on the Web with high availability Ruben Verborgh, Olaf Hartig, Ben De Meester, Gerald Haesendonck, Laurens De Vocht, Miel Vander Sande, Richard Cyganiak, Pieter Colpaert, Erik Mannens, and Rik Van de Walle
  • 2. Somebody comes to you with a high-quality Linked Open Data set: “How can people query my Linked Data on the Web?”
  • 3. “A public SPARQL endpoint gives live querying, but it’s costly and has availability issues.” “Offer a data dump. but it’s not really Web querying: users need to set up an endpoint” “Publish Linked Data documents. But querying is very slow…”
  • 4. Querying Linked Data on the Web always involves trade-offs. But have we looked at all possible trade-offs?
  • 5. Querying Linked Data live on the Web becomes affordable by building simpler servers and more intelligent clients.
  • 6. We formalized and evaluated client-side SPARQL querying through a triple-pattern interface. The trade-offs are different: low-cost, high-availability servers, more bandwidth and slower queries.
  • 7. Querying datasets on the Web with high availability Trade-offs for the Semantic Web Triple pattern fragment querying Evaluating the trade-offs
  • 8. Querying datasets on the Web with high availability Trade-offs for the Semantic Web Triple pattern fragment querying Evaluating the trade-offs
  • 9. It is not an all-or-nothing world. There is a spectrum of trade-offs. out-of-date data live data high bandwidth low bandwidth high availability low availability high client cost low client cost low server cost high server cost data dump SPARQL endpoint Linked Data documents interface offered by the server
  • 10. Linked Data Fragments are a uniform view on Linked Data interfaces. data dump SPARQL endpoint Every Linked Data interface offers specific fragments of a Linked Data set. Linked Data documents interface offered by the server
  • 11. Each type of Linked Data Fragment is defined by three characteristics. data metadata controls What triples does it contain? What do we know about it? How to access more data?
  • 12. Each type of Linked Data Fragment is defined by three characteristics. all dataset triples (none) data dump number of triples, file size data metadata controls
  • 13. Each type of Linked Data Fragment is defined by three characteristics. SPARQL query result data triples matching the query (none) (none) metadata controls
  • 14. Each type of Linked Data Fragment is defined by three characteristics. Linked Data Document data triples about a subject creator, maintainer, … links to other LD documents metadata controls
  • 15. We designed a new trade-off mix with low cost and high availability. out-of-date data live data high bandwidth low bandwidth high availability low availability high client cost low client cost low server cost high server cost data dump SPARQL query results Linked Data documents
  • 16. A triple pattern fragments interface is low-cost and enables clients to query. live data low server cost data dump SPARQL query results high availability Linked Data documents triple pattern fragments
  • 17. A triple pattern fragments interface is low-cost and enables clients to query. matches of a triple pattern total number of matches access to all other fragments data metadata controls (paged)
  • 18. controls (other fragments) metadata (total count) data (first 100)
  • 19. Triple patterns are not the final answer. No interface ever will be. Triple patterns show how far we can get with simple servers and smart clients. data dump SPARQL query results Linked Data documents triple pattern fragments
  • 20. Querying datasets on the Web with high availability Trade-offs for the Semantic Web Triple pattern fragment querying Evaluating the trade-offs
  • 21. Triple pattern fragment servers enable clients to be intelligent. The HTML representation explains: “you can query by triple pattern”. controls
  • 22. Triple pattern fragment servers enable clients to be intelligent. <http://fragments.dbpedia.org/2014/en#dataset> hydra:search [ hydra:template "http://fragments.dbpedia.org/2014/en controls {?subject,predicate,object}"; hydra:mapping [ hydra:variable "subject"; hydra:property rdf:subject ], [ hydra:variable "predicate"; hydra:property rdf:predicate ], [ hydra:variable "object"; hydra:property rdf:object ] ]. The RDF representation explains: “you can query by triple pattern”.
  • 23. Triple pattern fragment servers enable clients to be intelligent. The HTML representation explains: “this is the number of matches”. metadata
  • 24. Triple pattern fragment servers enable clients to be intelligent. <#fragment> void:triples 8141. The RDF representation explains: “this is the number of matches”. metadata
  • 25. How can intelligent clients solve SPARQL queries over fragments? Give them a SPARQL query. Give them a URL of any dataset fragment. They look inside the fragment to see how to access the dataset and use the metadata to decide how to plan the query.
  • 26. Let’s follow the execution of an example SPARQL query. Find names of artists born in Padua, Italy. SELECT ?artist ?name WHERE { ?artist a dbpedia-owl:Artist; rdfs:label ?name; dbpedia-owl:birthPlace dbpedia:Padua. FILTER LANGMATCHES(LANG(?name), "EN") } Fragment: http://fragments.dbpedia.org/2014/en
  • 27. The client looks inside the fragment to see how to access the dataset. Fragment: http://fragments.dbpedia.org/2014/en <http://fragments.dbpedia.org/2014/en#dataset> hydra:search [ hydra:template "http://fragments.dbpedia.org/2014/en {?subject,predicate,object}"; hydra:mapping [ hydra:variable "subject"; hydra:property rdf:subject ], [ hydra:variable "predicate"; hydra:property rdf:predicate ], [ hydra:variable "object"; hydra:property rdf:object ] ]. “I can query the dataset by triple pattern.”
  • 28. The client splits the query into the available fragments. SELECT ?artist ?name WHERE { ?artist a dbpedia-owl:Artist; rdfs:label ?name; dbpedia-owl:birthPlace dbpedia:Padua. FILTER LANGMATCHES(LANG(?name), "EN") }
  • 29. The client gets the fragments and inspects their metadata. ?artist a dbpedia-owl:Artist. first 100 triples 96.000 ?artist rdfs:label ?name. first 100 triples 12.000.000 ?artist dbont:birthPlace dbpedia:Padua. first 100 triples 135
  • 30. The metadata enables the client to choose the right starting point. ?dbp:artist Alberto_a dbpedia-Benettin owl:a Artist. dbont:Artist. 96.000 ?artist rdfs:label ?name. 12.000.000 dbp:Alberto_Benettin rdfs:label ?name. ?artist dbont:birthPlace dbpedia:Padua. 135 dbpedia:Alberto_Benettin dbont:birthPlace dbpedia:Padua. dbpedia:Alberto_Bigon dbont:birthPlace dbpedia:Padua.
  • 31. Clients execute the query in 3 seconds on a highly available, low-cost server. SELECT ?artist ?name WHERE { ?artist a dbpedia-owl:Artist; rdfs:label ?name; dbpedia-owl:birthPlace dbpedia:Padua. FILTER LANGMATCHES(LANG(?name), "EN") } Try it yourself: bit.ly/artistspadua
  • 32. Querying datasets on the Web with high availability Trade-offs for the Semantic Web Triple pattern fragment querying Evaluating the trade-offs
  • 33. We evaluated triple pattern fragments for server cost and availability. Berlin SPARQL benchmark on Amazon EC2 machines 100 million triples high query diversity BGP, UNION, FILTER, …
  • 34. We evaluated triple pattern fragments for server cost and availability. Berlin SPARQL benchmark on Amazon EC2 machines 1 server 1 cache 1–240 simultaneous clients
  • 35. The query throughput is lower, but resilient to high client numbers. Querying Datasets on 1 10 100 10 100 1000 10000 clients throughput (q/hr) Virtuoso 6 Fuseki–tdb triple pattern Fig. 3.1: Server performance (log-log plot) 200 executed SPARQL queries per hour timeouts
  • 36. The server traffic is higher, but requests are significantly lighter. Datasets on the Web with High Availability 13 Virtuoso 6 Virtuoso 7 Fuseki–tdb Fuseki–hdt pattern fragments 1 10 100 4 2 0 clients data sent (mb) Fig. 3.2: Server network traffic data sent by server in MB
  • 37. 2 Caching is significantly more effective, as clients reuse fragments for queries. 1 10 100 0 clients sent (mb) Fig. 3.2: Server network traffic 1 10 100 20 10 0 clients sent (mb) Fig. 3.4: Cache network traffic ram use data sent by cache in MB 8 6 4
  • 38. 150 The server uses much less CPU, allowing for higher availability. clients 1 10 100 1 1 10 100 100 50 0 100 50 1 50 server CPU usage per core #timeouts Fig. 3.3: Query timeouts 0 clients cpu use (%) Fig. 3.5: Server processor usage per core 100 use (%)
  • 39. 150 Servers enable clients to be intelligent, so they remain simple and light-weight. 1 10 100 1 1 10 100 100 50 0 clients #timeouts Fig. 3.3: Query timeouts 100 50 0 clients cpu use (%) 1 Fig. 3.5: Server processor usage per core 50 100 use (%) server CPU usage per core
  • 40. Querying datasets on the Web with high availability Trade-offs for the Semantic Web Triple pattern fragment querying Evaluating the trade-offs
  • 41. Triple pattern fragments allow querying live data with high availability. live data low server cost data dump SPARQL query results high availability Linked Data documents triple pattern fragments
  • 42. Like each Linked Data query method, triple pattern fragments have trade-offs. Live data Low-cost and highly available servers More bandwidth Slower results (but they are streaming)
  • 43. Experience the trade-offs yourself on the official DBpedia interfaces. DBpedia data dump DBpedia Linked Data documents DBpedia SPARQL endpoint DBpedia triple pattern fragments fragments.dbpedia.org
  • 44. Triple pattern fragments are easy: all software is available as open source. Software github.com/LinkedDataFragments Documentation and specification linkeddatafragments.org
  • 45. Querying is a client–server dialogue. Start exploring the entire spectrum. data dump SPARQL query results Linked Data documents triple pattern fragments
  • 46. Querying datasets on the Web with high availability linkeddatafragments.org @LDFragments