Presentation of the paper "Creating 3rd Generation Web APIs with Hydra" at the 22nd Internation World Wide Web Conference (WWW2013) in Rio de Janeiro, Brazil
Invited talk at USEWOD2014 (http://people.cs.kuleuven.be/~bettina.berendt/USEWOD2014/)
A tremendous amount of machine-interpretable information is available in the Linked Open Data Cloud. Unfortunately, much of this data remains underused as machine clients struggle to use the Web. I believe this can be solved by giving machines interfaces similar to those we offer humans, instead of separate interfaces such as SPARQL endpoints. In this talk, I'll discuss the Linked Data Fragments vision on machine access to the Web of Data, and indicate how this impacts usage analysis of the LOD Cloud. We all can learn a lot from how humans access the Web, and those strategies can be applied to querying and analysis. In particular, we have to focus first on solving those use cases that humans can do easily, and only then consider tackling others.
Presentation of the paper "Creating 3rd Generation Web APIs with Hydra" at the 22nd Internation World Wide Web Conference (WWW2013) in Rio de Janeiro, Brazil
Invited talk at USEWOD2014 (http://people.cs.kuleuven.be/~bettina.berendt/USEWOD2014/)
A tremendous amount of machine-interpretable information is available in the Linked Open Data Cloud. Unfortunately, much of this data remains underused as machine clients struggle to use the Web. I believe this can be solved by giving machines interfaces similar to those we offer humans, instead of separate interfaces such as SPARQL endpoints. In this talk, I'll discuss the Linked Data Fragments vision on machine access to the Web of Data, and indicate how this impacts usage analysis of the LOD Cloud. We all can learn a lot from how humans access the Web, and those strategies can be applied to querying and analysis. In particular, we have to focus first on solving those use cases that humans can do easily, and only then consider tackling others.
Opportunistic Linked Data Querying through Approximate Membership MetadataMiel Vander Sande
Between uri dereferencing and the sparql protocol lies a largely
unexplored axis of possible interfaces to Linked Data, eachwith its own combination of trade-offs. One of these interfaces is Triple Pattern Fragments, which allows clients to execute sparql queries against low-cost servers, at the cost of higher bandwidth. Increasing a client’s efficiency means lowering the number of requests, which can among others be achieved through additional metadata in responses.
We noted that typical sparql query evaluations against Triple Pattern Fragments
require a significant portion of membership subqueries, which check the presence
of a specific triple, rather than a variable pattern. This paper studies the impact
of providing approximate membership functions, i.e., Bloom filters and Golombcoded
sets, as extra metadata. In addition to reducing http requests, such functions
allow to achieve full result recall earlier when temporarily allowing lower precision.
Half of the tested queries from aWatDiv benchmark test set could be executed with
up to a third fewer http requests with only marginally higher server cost. Query
times, however, did not improve, likely due to slower metadata generation and
transfer. This indicates that approximate membership functions can partly improve
the client-side query process with minimal impact on the server and its interface.
Finding knowledge, data and answers on the Semantic Webebiquity
Web search engines like Google have made us all smarter by providing ready access to the world's knowledge whenever we need to look up a fact, learn about a topic or evaluate opinions. The W3C's Semantic Web effort aims to make such knowledge more accessible to computer programs by publishing it in machine understandable form.
<p>
As the volume of Semantic Web data grows software agents will need their own search engines to help them find the relevant and trustworthy knowledge they need to perform their tasks. We will discuss the general issues underlying the indexing and retrieval of RDF based information and describe Swoogle, a crawler based search engine whose index contains information on over a million RDF documents.
<p>
We will illustrate its use in several Semantic Web related research projects at UMBC including a distributed platform for constructing end-to-end use cases that demonstrate the semantic web’s utility for integrating scientific data. We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location, and Triple Shop, a SPARQL query interface which searches the Semantic Web for data relevant to a given query ELVIS functionality is exposed as a collection of web services, and all input and output data is expressed in OWL, thereby enabling its integration with Triple Shop and other semantic web resources.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
ParlBench: a SPARQL-benchmark for electronic publishing applications.Tatiana Tarasova
Slides from the workshop on Benchmarking RDF Systems co-located with the Extended Semantic Web Conference 2013. The presentation is about an on-going work on building the benchmark for electronic publishing applications. The benchmark provides real-world data sets, the Dutch parliamentary proceedings and a set of analytical SPARQL queries that were built on top of these data sets. The queries were grouped into micro-benchmarks according to their analytical aims. This allows one to perform better analysis of RDF stores behaviors with respect to a certain SPARQL feature used in a micro-benchmark/query.
Preliminary results of running the benchmark on the Virtuoso native RDF store are presented, as well as references to the on-line material including the data sets, queries and the scripts that were used to obtain the results.
Declarative Multilingual Information Extraction with SystemTLaura Chiticariu
Information extraction (IE), the task of extracting structured information from unstructured or semi-structured data, is increasingly important to a wide array of enterprise applications, ranging from Business Intelligence to Data-as-a-Service.
In the first part of the talk, we give an overview of SystemT, a declarative IE system designed and developed to address the requirements driven by modern applications: scalability, expressivity, and transparency. SystemT is based on the basic principle underlying relational database technology: complete separation of specification from execution. SystemT uses a declarative language for expressing NLP algorithms called AQL, and an optimizer that generates high-performance algebraic execution plans for AQL rules. It makes IE orders of magnitude more scalable and easy to use, maintain and customize. Today, SystemT ships with multiple products across 4 IBM Software Brands and it being taught in universities. Our ongoing research and development efforts focus on making SystemT more usable for both technical and business users, and continuing enhancing its core functionalities based on natural language processing, machine learning, and database technology.
In the second part of the talk we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. The key feature of the system is that it treats the semantic labels of the English Proposition Bank as “universal semantic labels”: Given a sentence in any of the supported languages, POLYGLOT will predict appropriate English PropBank frame and role annotation. We illustrate how these universal semantic labels can be used within SystemT to create information extractors that immediately work across different languages. In addition, we illustrate how we automatically generate Proposition Banks for new languages in order to enable multilingual SRL and discuss some challenges of crosslingual semantics.
Question Answering over Linked Data - Reasoning IssuesMichael Petychakis
Question answering system plays a vital role in search engine optimization model. Natural language processing methods are typically applied in QA system for inquiring user’s question and numerous steps are also followed for alteration of questions to query form for receiving a precise answer. This presentation analyzes diverse question answering systems that are based on semantic web technologies and ontologies with different formats of queries.It ends by addressing various reasoning alternatives.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Querying data on the Web – client or server?
1. Querying data on the Web:
client or server?
Ruben Verborgh
Ghent University – iMinds
2. The current Semantic Web
has many implicit assumptions.
We should be able
to answer all queries.
Complexity is more important
than availability.
Data servers
need to be expensive.
4. Some queries are
hard to answer.
Availability is
a top priority.
Low-cost data servers
have potential.
Let’s rethink our assumptions,
just to see what’s possible.
9. The Web for humans offers
an HTTP interface to HTML.
client dataHTTP
HTML
10. The Web for applications offers
an HTTP interface to JSON.
client dataHTTP
JSON
11. The Web for applications offers
an HTTP interface to RDF.
client dataHTTP
RDF
12. The Web for applications offers
an SPARQL interface to RDF.
client dataHTTP
RDF
SPARQL
13. Documents need a new language.
Semantic Web clients were
perceived as very limited.
Querying needs a new protocol.
…unlike “simple” JSON clients.
14. 1. Clients need a different protocol.
2. Live queries require that protocol.
15. public SPARQL endpoints
There are 3 common ways
to publish Linked Data.
Linked Data documents
downloadable data dumps
16. …and that’s not always a good thing.
Public SPARQL endpoints
offer a very powerful interface.
Clients can ask any query…
…if the endpoint is available.
Hosting an endpoint is costly.
17. Low-cost to host.
Linked Data documents
seem to work like the Web.
Solve queries by traversing links.
Many queries cannot be solved.
18. Set up your own endpoint.
Downloadable data dumps
have high availability.
Data is not live.
You’re not really querying the Web.
19. 1. Clients need a different protocol.
2. Live queries require that protocol.
3. Clients can request any query.
20. The query language abstracts away
the steps needed to solve it.
In SPARQL, asking a simple query
is as easy as asking a difficult one.
In contrast to the rest of the Web,
clients are in control.
21. With a JSON interface, the server
decides how clients access data.
client dataHTTP
JSON
23. Clients can ask anything, also
queries that bring servers down.
The majority
of public SPARQL endpoints
has less than 95% availability.
That means the endpoint
—and thus your application—
doesn’t work 1.5 days each month.
24. If you have operational need
for SPARQL accessible data,
you must have your own infrastructure.
No public endpoints.
Public endpoints are for lookups and discovery;
sort of a dataset demo.
—Orri Erling, OpenLink (2014)
26. If you want to study
a subject on Wikipedia,
do you download all
4,614,000 articles first?
27. 1. Clients need a different protocol.
2. Live queries require that protocol.
3. Clients can request any query.
28. The Semantic Web’s assumptions
Client-side query execution
New query opportunities
Querying data on the Web:
client or server?
29. data
dump
SPARQL
endpoint
Any fragment of a Linked Data set
is called a Linked Data Fragment.
derefer-
encing
high server efforthigh client effort
all subject SPARQL querySELECTOR
30. Each type of Linked Data Fragment
is defined by three characteristics.
selector
metadata
controls
What data does it contain?
What do we know about it?
What can we do next?
31. a SPARQL query
(none)
(none)
SPARQL CONSTRUCT result
selector
metadata
controls
Each type of Linked Data Fragment
is defined by three characteristics.
32. a specific entity
creator, maintainer, …
links to other LD documents
Linked Data Document
selector
metadata
controls
Each type of Linked Data Fragment
is defined by three characteristics.
33. everything
(none)
data dump
number of triples, file size
selector
metadata
controls
Each type of Linked Data Fragment
is defined by three characteristics.
34. Can we query fragments that
balance client and server effort?
data
dump
SPARQL
endpoint
triple
pattern
fragments
derefer-
encing
high server efforthigh client effort
all subject SPARQL querytriple pattern
35. triple pattern
total number of matches
access to all other fragments
selector
metadata
controls
Triple pattern fragments are cheap
yet enable efficient querying.
37. Other APIs exist, but are specific.
Triple pattern fragment servers
enable clients to execute queries.
Triple patterns work on all datasets.
Combine data, metadata & controls.
38. How to answer this query using
only triple pattern fragments?
SELECT ?person ?city WHERE {
?person a dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "York"@en.
}
39. Get the corresponding fragments
?person a dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "York"@en.
dbpedia:York foaf:name “York”@en.
dbpedia:York,_Ontario foaf:name “York”@en.
…
dbpedia:Ganesh_Ghosh …:birthPlace dbpedia:Bengal_Presidency.
dbpedia:Jacques_L'enfant …:birthPlace dbpedia:Beauce.
…
dbpedia:Aamir_Zaki a dbpedia-owl:Artist.
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
40. Get the corresponding fragments
and read the count metadata.
?person a dbpedia-owl:Artist. ±61,000
±470,000
12
?person dbpedia-owl:birthPlace ?city.
?city foaf:name "York"@en.
dbpedia:York foaf:name “York”@en.
dbpedia:York,_Ontario foaf:name “York”@en.
…
dbpedia:Ganesh_Ghosh …:birthPlace dbpedia:Bengal_Presidency.
dbpedia:Jacques_L'enfant …:birthPlace dbpedia:Beauce.
…
dbpedia:Aamir_Zaki a dbpedia-owl:Artist.
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
41. Start with the smallest fragment.
Start with the first match.
?person a dbpedia-owl:Artist ±61,
±470,
12
?person dbpedia-owl:birthPlace
?city foaf:name "York"@en.
dbpedia:York foaf:name “York”@en.
dbpedia:York,_Ontario foaf:name “York”@en.
…
dbpedia:Ganesh_Ghosh …:birthPlace dbpedia:Bengal_Presidency.
dbpedia:Jacques_L'enfant …:birthPlace dbpedia:Beauce.
…
dbpedia:Aamir_Zaki
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
42. How to answer this query using
only triple pattern fragments?
SELECT ?person WHERE {
?person a dbpedia-owl:Artist.
?person dbpedia-owl:birthPlace dbpedia:York.
dbpedia:York foaf:name "York"@en.
}
43. Get the corresponding fragments
?person a dbpedia-owl:Artist.
?person dbpo:birthPlace dbpedia:York.
dbpedia:John_Flaxman dbpo:birthPlace dbpedia:York.
dbpedia:Joseph_Hansom dbpo:birthPlace dbpedia:York.
…
dbpedia:Aamir_Zaki a dbpedia-owl:Artist.
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
44. Get the corresponding fragments
and read the count metadata.
?person a dbpedia-owl:Artist. ±61,000
75?person dbpo:birthPlace dbpedia:York.
dbpedia:John_Flaxman dbpo:birthPlace dbpedia:York.
dbpedia:Joseph_Hansom dbpo:birthPlace dbpedia:York.
…
dbpedia:Aamir_Zaki a dbpedia-owl:Artist.
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
45. Start with the smallest fragment.
Start with the first match.
?person a dbpedia-owl:Artist ±61,
75?person dbpo:birthPlace dbpedia:York.
dbpedia:John_Flaxman dbpo:birthPlace dbpedia:York.
dbpedia:Joseph_Hansom dbpo:birthPlace dbpedia:York.
…
dbpedia:Aamir_Zaki
dbpedia:Ahmad_Morid a dbpedia-owl:Artist.
…
46. How to answer this query using
only triple pattern fragments?
ASK {
dbp:John_Flaxman a dbpo:Artist.
dbp:John_Flaxman dbpo:birthPlace dbp:York.
dbp:York foaf:name "York"@en.
}
47. Get the corresponding fragment
and read the count metadata.
dbpedia:John_Flaxman a dbpedia-owl:Artist. 1
dbpedia:John_Flaxman a dbpedia-owl:Artist.
!
Output the match:
?person = dbpedia:John_Flaxman
?city = dbpedia:York
48. Recursively repeat the process
for all bindings.
?person dbpo:birthPlace dbpedia:York.
dbpedia:John_Flaxman dbpo:birthPlace dbpedia:York.
dbpedia:Joseph_Hansom dbpo:birthPlace dbpedia:York.
…
?city foaf:name "York"@en.
dbpedia:York foaf:name “York”@en.
dbpedia:York,_Ontario foaf:name “York”@en.
…
49. Use the Web’s protocol HTTP.
This way of querying
changes the usual assumptions.
Don’t be smart; enable intelligence.
Some queries will be hard / slow.
50. Querying semantic datasources
means managing expectations.
data
dump
SPARQL
endpoint
triple
pattern
fragments
derefer-
encing
high server efforthigh client effort
low availabilityhigh availability
low freshness / speed high freshness / speed
51. The Semantic Web’s assumptions
Client-side query execution
New query opportunities
Querying data on the Web:
client or server?
52. Coupling access and processing
leads to low availability.
SPARQL Server
Client
Client
Client
Client
Client
Client
Client
(a) sparql endpoints perform all processing on the server, leading to fast
query execution with low data bandwidth, and a rapidly overloaded server.
57. endpoint
approach
Show a sorted list of molecules
that match certain characteristics.
SELECT DISTINCT(?mol) MIN(?name)
WHERE {
?mol rdfs:label ?name;
…
…
}
ORDER BY ?name
58. endpoint
approach
DISTINCT
MIN
SORT BY
keep all results in memory
keep all results in memory, blocking
keep all results in memory, blocking
Consequences:
Doesn’t matter; we’re waiting anyway.
Show a sorted list of molecules
that match certain characteristics.
59. fragments
approach
No blocking operators; streaming matters.
Show a sorted list of molecules
that match certain characteristics.
SELECT ?mol ?name
WHERE {
?mol rdfs:label ?name;
…
…
}
61. The algorithm remains the same
when clients use one or multiple
triple pattern fragment servers.
Federation also becomes
substantially easier.
Avoid the unavailability cascade.
62. An optimal solution doesn’t exist.
We should look at all APIs.
data
dump
SPARQL
endpoint
triple
pattern
fragments
derefer-
encing
63. Servers indicate what they do,
enabling clients to query optimally.
“This server supports triple patterns
and full-text search on objects.”
“This server supports SPARQL queries
with up to 2 joins.”
“This server supports Linked Data documents.”
64. The Semantic Web’s assumptions
Client-side query execution
New query opportunities
Querying data on the Web:
client or server?
65. Different assumptions
lead to different trade-offs.
Live querying of public data
is possible at low cost,
but at slower speeds…
…for now :-)
66. Let your browser
solve a SPARQL query:
client.linkeddatafragments.org
Ruben Verborgh
Ghent University – iMinds