SlideShare a Scribd company logo

Continuously Updating Query Results over Real-Time Linked Data

Presentation for the MEPDaW workshop @ESWC 2016

1 of 21
Download to read offline
Ruben Taelman - @rubensworks
iMinds - Ghent University
Continuously Updating Query Results
over Real-Time Linked Data
Dynamic Linked Data
E.g. Thermometer measures every minute:
“19,05°C” - 30-05-2016 11:00
“19,06°C” - 30-05-2016 11:01
“19,11°C” - 30-05-2016 11:02
“19,08°C” - 30-05-2016 11:03
…
Typically exposed as an RDF stream = stream of <RDF triple, timestamp>
Querying continous data
Clients send queries to server: e.g. What is the current temperature?
Server continuously evaluates the queries
→ Server does all of the work
Cause of low public endpoint availability!
½ have availability of < 95% (Buil-Aranda 2013)
→ Clients just wait for results
What if we moved continuous query evaluation to the client?
→ to lower server load
Triple Pattern Fragments does this for static data!
Triple pattern fragments (TPF) (Verborgh 2016):
Servers can only respond to triple pattern queries
Clients need to evaluate queries locally
→ Lowers server complexity
Can we do the same for dynamic data?
Overview
Dynamic data representation
Query streamer engine
Evaluation

Recommended

Continuous Self-Updating Query Results over Dynamic Linked Data
Continuous Self-Updating Query Results over Dynamic Linked DataContinuous Self-Updating Query Results over Dynamic Linked Data
Continuous Self-Updating Query Results over Dynamic Linked DataRuben Taelman
 
Querying Dynamic Datasources with Continuously Mapped Sensor Data
Querying Dynamic Datasources with Continuously Mapped Sensor DataQuerying Dynamic Datasources with Continuously Mapped Sensor Data
Querying Dynamic Datasources with Continuously Mapped Sensor DataRuben Taelman
 
Moving RDF Stream Processing to the Client
Moving RDF Stream Processing to the ClientMoving RDF Stream Processing to the Client
Moving RDF Stream Processing to the ClientRuben Taelman
 
Scalable Dynamic Data Consumption on the Web
Scalable Dynamic Data Consumption on the WebScalable Dynamic Data Consumption on the Web
Scalable Dynamic Data Consumption on the WebRuben Taelman
 
Flink Forward Berlin 2017: Francesco Versaci - Integrating Flink and Kafka in...
Flink Forward Berlin 2017: Francesco Versaci - Integrating Flink and Kafka in...Flink Forward Berlin 2017: Francesco Versaci - Integrating Flink and Kafka in...
Flink Forward Berlin 2017: Francesco Versaci - Integrating Flink and Kafka in...Flink Forward
 
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...Flink Forward
 
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward
 

More Related Content

What's hot

Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...Ververica
 
Ceilometer lsf-intergration-openstack-summit
Ceilometer lsf-intergration-openstack-summitCeilometer lsf-intergration-openstack-summit
Ceilometer lsf-intergration-openstack-summitTim Bell
 
Apache Flink's Table & SQL API - unified APIs for batch and stream processing
Apache Flink's Table & SQL API - unified APIs for batch and stream processingApache Flink's Table & SQL API - unified APIs for batch and stream processing
Apache Flink's Table & SQL API - unified APIs for batch and stream processingTimo Walther
 
Stream Analytics with SQL on Apache Flink
Stream Analytics with SQL on Apache FlinkStream Analytics with SQL on Apache Flink
Stream Analytics with SQL on Apache FlinkFabian Hueske
 
Redis Day TLV 2018 - RediSearch Aggregations
Redis Day TLV 2018 - RediSearch AggregationsRedis Day TLV 2018 - RediSearch Aggregations
Redis Day TLV 2018 - RediSearch AggregationsRedis Labs
 
Redis Day TLV 2018 - Redis as a Time-Series DB
Redis Day TLV 2018 - Redis as a Time-Series DBRedis Day TLV 2018 - Redis as a Time-Series DB
Redis Day TLV 2018 - Redis as a Time-Series DBRedis Labs
 
WHODIS_kearns_presentation.v0a
WHODIS_kearns_presentation.v0aWHODIS_kearns_presentation.v0a
WHODIS_kearns_presentation.v0aEdward Kearns
 
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...Ververica
 
C* Summit EU 2013: Analytics On Top of Cassandra and Hadoop
C* Summit EU 2013: Analytics On Top of Cassandra and HadoopC* Summit EU 2013: Analytics On Top of Cassandra and Hadoop
C* Summit EU 2013: Analytics On Top of Cassandra and HadoopDataStax Academy
 
Running a MapReduce job on AWS
Running a MapReduce job on AWSRunning a MapReduce job on AWS
Running a MapReduce job on AWSToshiaki Takeuchi
 
Join semantics in kafka streams
Join semantics in kafka streamsJoin semantics in kafka streams
Join semantics in kafka streamsKnoldus Inc.
 
Gyula Fóra - RBEA- Scalable Real-Time Analytics at King
Gyula Fóra - RBEA- Scalable Real-Time Analytics at KingGyula Fóra - RBEA- Scalable Real-Time Analytics at King
Gyula Fóra - RBEA- Scalable Real-Time Analytics at KingFlink Forward
 
Fabian Hueske - Stream Analytics with SQL on Apache Flink
Fabian Hueske - Stream Analytics with SQL on Apache FlinkFabian Hueske - Stream Analytics with SQL on Apache Flink
Fabian Hueske - Stream Analytics with SQL on Apache FlinkVerverica
 
Load Balancing in Cloud Computing Thesis Research Help
Load Balancing in Cloud Computing Thesis Research HelpLoad Balancing in Cloud Computing Thesis Research Help
Load Balancing in Cloud Computing Thesis Research HelpPhdtopiccom
 
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...Jonas Traub
 

What's hot (19)

Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...
 
Ceilometer lsf-intergration-openstack-summit
Ceilometer lsf-intergration-openstack-summitCeilometer lsf-intergration-openstack-summit
Ceilometer lsf-intergration-openstack-summit
 
Apache Flink's Table & SQL API - unified APIs for batch and stream processing
Apache Flink's Table & SQL API - unified APIs for batch and stream processingApache Flink's Table & SQL API - unified APIs for batch and stream processing
Apache Flink's Table & SQL API - unified APIs for batch and stream processing
 
Stream Analytics with SQL on Apache Flink
Stream Analytics with SQL on Apache FlinkStream Analytics with SQL on Apache Flink
Stream Analytics with SQL on Apache Flink
 
Prometheus on AWS
Prometheus on AWSPrometheus on AWS
Prometheus on AWS
 
Redis Day TLV 2018 - RediSearch Aggregations
Redis Day TLV 2018 - RediSearch AggregationsRedis Day TLV 2018 - RediSearch Aggregations
Redis Day TLV 2018 - RediSearch Aggregations
 
Redis Day TLV 2018 - Redis as a Time-Series DB
Redis Day TLV 2018 - Redis as a Time-Series DBRedis Day TLV 2018 - Redis as a Time-Series DB
Redis Day TLV 2018 - Redis as a Time-Series DB
 
WHODIS_kearns_presentation.v0a
WHODIS_kearns_presentation.v0aWHODIS_kearns_presentation.v0a
WHODIS_kearns_presentation.v0a
 
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...
Stephan Ewen - Stream Processing as a Foundational Paradigm and Apache Flink'...
 
Monitoring with riemann
Monitoring with riemannMonitoring with riemann
Monitoring with riemann
 
C* Summit EU 2013: Analytics On Top of Cassandra and Hadoop
C* Summit EU 2013: Analytics On Top of Cassandra and HadoopC* Summit EU 2013: Analytics On Top of Cassandra and Hadoop
C* Summit EU 2013: Analytics On Top of Cassandra and Hadoop
 
Intoduce Xephon-B
Intoduce Xephon-B Intoduce Xephon-B
Intoduce Xephon-B
 
Running a MapReduce job on AWS
Running a MapReduce job on AWSRunning a MapReduce job on AWS
Running a MapReduce job on AWS
 
Join semantics in kafka streams
Join semantics in kafka streamsJoin semantics in kafka streams
Join semantics in kafka streams
 
Qtp testing23
Qtp testing23Qtp testing23
Qtp testing23
 
Gyula Fóra - RBEA- Scalable Real-Time Analytics at King
Gyula Fóra - RBEA- Scalable Real-Time Analytics at KingGyula Fóra - RBEA- Scalable Real-Time Analytics at King
Gyula Fóra - RBEA- Scalable Real-Time Analytics at King
 
Fabian Hueske - Stream Analytics with SQL on Apache Flink
Fabian Hueske - Stream Analytics with SQL on Apache FlinkFabian Hueske - Stream Analytics with SQL on Apache Flink
Fabian Hueske - Stream Analytics with SQL on Apache Flink
 
Load Balancing in Cloud Computing Thesis Research Help
Load Balancing in Cloud Computing Thesis Research HelpLoad Balancing in Cloud Computing Thesis Research Help
Load Balancing in Cloud Computing Thesis Research Help
 
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
 

Viewers also liked

Kent English Profile
Kent English ProfileKent English Profile
Kent English ProfileRex Kent Liu
 
EKAW - Linked Data Publishing
EKAW - Linked Data PublishingEKAW - Linked Data Publishing
EKAW - Linked Data PublishingRuben Taelman
 
PowerPoint Presentation.2015
PowerPoint Presentation.2015PowerPoint Presentation.2015
PowerPoint Presentation.2015Samar Kamel
 
Camera Angles
Camera AnglesCamera Angles
Camera AnglesJamesElam
 
The Demon Final
The Demon FinalThe Demon Final
The Demon FinalJamesElam
 
EKAW - Triple Pattern Fragments
EKAW - Triple Pattern FragmentsEKAW - Triple Pattern Fragments
EKAW - Triple Pattern FragmentsRuben Taelman
 
Tienda motor store
Tienda motor storeTienda motor store
Tienda motor storeAmaiitaa
 
Penguat transistor
Penguat transistorPenguat transistor
Penguat transistormz_khamim
 
Trade commodity finance and its services
Trade commodity finance and its servicesTrade commodity finance and its services
Trade commodity finance and its servicesRusca Dimitri
 
Computer aided analysis and design of multi story building
Computer aided analysis and design of multi story buildingComputer aided analysis and design of multi story building
Computer aided analysis and design of multi story buildingparas6904
 

Viewers also liked (15)

Kent English Profile
Kent English ProfileKent English Profile
Kent English Profile
 
EKAW - Linked Data Publishing
EKAW - Linked Data PublishingEKAW - Linked Data Publishing
EKAW - Linked Data Publishing
 
PowerPoint Presentation.2015
PowerPoint Presentation.2015PowerPoint Presentation.2015
PowerPoint Presentation.2015
 
Abhishek
AbhishekAbhishek
Abhishek
 
Camera Angles
Camera AnglesCamera Angles
Camera Angles
 
Jelly Shots
Jelly ShotsJelly Shots
Jelly Shots
 
The Demon Final
The Demon FinalThe Demon Final
The Demon Final
 
EKAW - Triple Pattern Fragments
EKAW - Triple Pattern FragmentsEKAW - Triple Pattern Fragments
EKAW - Triple Pattern Fragments
 
Tienda motor store
Tienda motor storeTienda motor store
Tienda motor store
 
Penguat transistor
Penguat transistorPenguat transistor
Penguat transistor
 
Nome - logo book
Nome  - logo bookNome  - logo book
Nome - logo book
 
Docker Intro
Docker IntroDocker Intro
Docker Intro
 
Flower lamp
Flower lampFlower lamp
Flower lamp
 
Trade commodity finance and its services
Trade commodity finance and its servicesTrade commodity finance and its services
Trade commodity finance and its services
 
Computer aided analysis and design of multi story building
Computer aided analysis and design of multi story buildingComputer aided analysis and design of multi story building
Computer aided analysis and design of multi story building
 

Similar to Continuously Updating Query Results over Real-Time Linked Data

Continuous Processing with Apache Flink - Strata London 2016
Continuous Processing with Apache Flink - Strata London 2016Continuous Processing with Apache Flink - Strata London 2016
Continuous Processing with Apache Flink - Strata London 2016Stephan Ewen
 
Have your cake and eat it too, further dispelling the myths of the lambda arc...
Have your cake and eat it too, further dispelling the myths of the lambda arc...Have your cake and eat it too, further dispelling the myths of the lambda arc...
Have your cake and eat it too, further dispelling the myths of the lambda arc...Dimos Raptis
 
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...Data Con LA
 
Parallel analytics as a service
Parallel analytics as a serviceParallel analytics as a service
Parallel analytics as a servicePetrie Wong
 
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink - Jonathan ...
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink -  Jonathan ...Flink Forward San Francisco 2019: The Trade Desk's Year in Flink -  Jonathan ...
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink - Jonathan ...Flink Forward
 
Automated Parameterization of Performance Models from Measurements
Automated Parameterization of Performance Models from MeasurementsAutomated Parameterization of Performance Models from Measurements
Automated Parameterization of Performance Models from MeasurementsWeikun Wang
 
Enhancing the NS-2 Traffic Generator for the MANETs
Enhancing the NS-2 Traffic Generator for the MANETsEnhancing the NS-2 Traffic Generator for the MANETs
Enhancing the NS-2 Traffic Generator for the MANETsIOSR Journals
 
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
 
An Introduction to Distributed Data Streaming
An Introduction to Distributed Data StreamingAn Introduction to Distributed Data Streaming
An Introduction to Distributed Data StreamingParis Carbone
 
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration PatternsGraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration PatternsNeo4j
 
IPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly
IPLC Analytic Dashboard - Mohd Rizal bin Mohd RamlyIPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly
IPLC Analytic Dashboard - Mohd Rizal bin Mohd RamlyMyNOG
 
Pdcs2010 balman-presentation
Pdcs2010 balman-presentationPdcs2010 balman-presentation
Pdcs2010 balman-presentationbalmanme
 
Streaming SQL to unify batch and stream processing: Theory and practice with ...
Streaming SQL to unify batch and stream processing: Theory and practice with ...Streaming SQL to unify batch and stream processing: Theory and practice with ...
Streaming SQL to unify batch and stream processing: Theory and practice with ...Fabian Hueske
 
Social Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadSocial Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadIoan Toma
 
Social Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadSocial Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadLDBC council
 
Meniscus Advanced Energy Analytics Platform
Meniscus Advanced Energy Analytics PlatformMeniscus Advanced Energy Analytics Platform
Meniscus Advanced Energy Analytics PlatformMike Everest
 
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009On Demand Time Sychronizaton for Wireless Sensor Networks-november2009
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009abhiumn
 
Query optimization for_sensor_networks
Query optimization for_sensor_networksQuery optimization for_sensor_networks
Query optimization for_sensor_networksHarshavardhan Achrekar
 
METRO NTD FINAL Presentation Last revision
METRO NTD FINAL Presentation Last revisionMETRO NTD FINAL Presentation Last revision
METRO NTD FINAL Presentation Last revisionRogelio Fonseca
 

Similar to Continuously Updating Query Results over Real-Time Linked Data (20)

Continuous Processing with Apache Flink - Strata London 2016
Continuous Processing with Apache Flink - Strata London 2016Continuous Processing with Apache Flink - Strata London 2016
Continuous Processing with Apache Flink - Strata London 2016
 
Have your cake and eat it too, further dispelling the myths of the lambda arc...
Have your cake and eat it too, further dispelling the myths of the lambda arc...Have your cake and eat it too, further dispelling the myths of the lambda arc...
Have your cake and eat it too, further dispelling the myths of the lambda arc...
 
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...
 
Parallel analytics as a service
Parallel analytics as a serviceParallel analytics as a service
Parallel analytics as a service
 
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink - Jonathan ...
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink -  Jonathan ...Flink Forward San Francisco 2019: The Trade Desk's Year in Flink -  Jonathan ...
Flink Forward San Francisco 2019: The Trade Desk's Year in Flink - Jonathan ...
 
Automated Parameterization of Performance Models from Measurements
Automated Parameterization of Performance Models from MeasurementsAutomated Parameterization of Performance Models from Measurements
Automated Parameterization of Performance Models from Measurements
 
Journal paper 1
Journal paper 1Journal paper 1
Journal paper 1
 
Enhancing the NS-2 Traffic Generator for the MANETs
Enhancing the NS-2 Traffic Generator for the MANETsEnhancing the NS-2 Traffic Generator for the MANETs
Enhancing the NS-2 Traffic Generator for the MANETs
 
Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...Optimization of Continuous Queries in Federated Database and Stream Processin...
Optimization of Continuous Queries in Federated Database and Stream Processin...
 
An Introduction to Distributed Data Streaming
An Introduction to Distributed Data StreamingAn Introduction to Distributed Data Streaming
An Introduction to Distributed Data Streaming
 
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration PatternsGraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
GraphConnect 2014 SF: Neo4j at Scale using Enterprise Integration Patterns
 
IPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly
IPLC Analytic Dashboard - Mohd Rizal bin Mohd RamlyIPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly
IPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly
 
Pdcs2010 balman-presentation
Pdcs2010 balman-presentationPdcs2010 balman-presentation
Pdcs2010 balman-presentation
 
Streaming SQL to unify batch and stream processing: Theory and practice with ...
Streaming SQL to unify batch and stream processing: Theory and practice with ...Streaming SQL to unify batch and stream processing: Theory and practice with ...
Streaming SQL to unify batch and stream processing: Theory and practice with ...
 
Social Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadSocial Network Benchmark Interactive Workload
Social Network Benchmark Interactive Workload
 
Social Network Benchmark Interactive Workload
Social Network Benchmark Interactive WorkloadSocial Network Benchmark Interactive Workload
Social Network Benchmark Interactive Workload
 
Meniscus Advanced Energy Analytics Platform
Meniscus Advanced Energy Analytics PlatformMeniscus Advanced Energy Analytics Platform
Meniscus Advanced Energy Analytics Platform
 
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009On Demand Time Sychronizaton for Wireless Sensor Networks-november2009
On Demand Time Sychronizaton for Wireless Sensor Networks-november2009
 
Query optimization for_sensor_networks
Query optimization for_sensor_networksQuery optimization for_sensor_networks
Query optimization for_sensor_networks
 
METRO NTD FINAL Presentation Last revision
METRO NTD FINAL Presentation Last revisionMETRO NTD FINAL Presentation Last revision
METRO NTD FINAL Presentation Last revision
 

More from Ruben Taelman

Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...
Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...
Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...Ruben Taelman
 
Poster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLPoster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLRuben Taelman
 
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsPoster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsRuben Taelman
 
Versioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsVersioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsRuben Taelman
 
Versioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsVersioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsRuben Taelman
 
PoDiGG: Public Transport Dataset Generator based on Population Distributions
PoDiGG: Public Transport Dataset Generator based on Population DistributionsPoDiGG: Public Transport Dataset Generator based on Population Distributions
PoDiGG: Public Transport Dataset Generator based on Population DistributionsRuben Taelman
 
Exposing RDF Archives using Triple Pattern Fragments
Exposing RDF Archives using Triple Pattern FragmentsExposing RDF Archives using Triple Pattern Fragments
Exposing RDF Archives using Triple Pattern FragmentsRuben Taelman
 
EKAW - Publishing with Triple Pattern Fragments
EKAW - Publishing with Triple Pattern FragmentsEKAW - Publishing with Triple Pattern Fragments
EKAW - Publishing with Triple Pattern FragmentsRuben Taelman
 
Multidimensional Interfaces for Selecting Data with Order
Multidimensional Interfaces for Selecting Data with OrderMultidimensional Interfaces for Selecting Data with Order
Multidimensional Interfaces for Selecting Data with OrderRuben Taelman
 

More from Ruben Taelman (10)

Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...
Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...
Poster Demonstration of Comunica, a Web framework for querying heterogeneous ...
 
Poster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQLPoster GraphQL-LD: Linked Data Querying with GraphQL
Poster GraphQL-LD: Linked Data Querying with GraphQL
 
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIsPoster Declaratively Describing Responses of Hypermedia-Driven Web APIs
Poster Declaratively Describing Responses of Hypermedia-Driven Web APIs
 
Components.js
Components.jsComponents.js
Components.js
 
Versioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsVersioned Triple Pattern Fragments
Versioned Triple Pattern Fragments
 
Versioned Triple Pattern Fragments
Versioned Triple Pattern FragmentsVersioned Triple Pattern Fragments
Versioned Triple Pattern Fragments
 
PoDiGG: Public Transport Dataset Generator based on Population Distributions
PoDiGG: Public Transport Dataset Generator based on Population DistributionsPoDiGG: Public Transport Dataset Generator based on Population Distributions
PoDiGG: Public Transport Dataset Generator based on Population Distributions
 
Exposing RDF Archives using Triple Pattern Fragments
Exposing RDF Archives using Triple Pattern FragmentsExposing RDF Archives using Triple Pattern Fragments
Exposing RDF Archives using Triple Pattern Fragments
 
EKAW - Publishing with Triple Pattern Fragments
EKAW - Publishing with Triple Pattern FragmentsEKAW - Publishing with Triple Pattern Fragments
EKAW - Publishing with Triple Pattern Fragments
 
Multidimensional Interfaces for Selecting Data with Order
Multidimensional Interfaces for Selecting Data with OrderMultidimensional Interfaces for Selecting Data with Order
Multidimensional Interfaces for Selecting Data with Order
 

Recently uploaded

MedTech R&D - Tamer Emara - resume @2024
MedTech R&D - Tamer Emara - resume @2024MedTech R&D - Tamer Emara - resume @2024
MedTech R&D - Tamer Emara - resume @2024Tamer Emara
 
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...C Sai Kiran
 
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdf
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdfForged Fitting Socket Welding Standard- ASME-B16.11-2001.pdf
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdfVikasKumar11936
 
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh Rajput
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh RajputBasic Instrumentation Symbols | P&ID | PFD | Gaurav Singh Rajput
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh RajputGaurav Singh Rajput
 
python presentation lists,strings,operation
python presentation lists,strings,operationpython presentation lists,strings,operation
python presentation lists,strings,operationManjuRaghavan1
 
Model Approved Food/ sanitary Grade Flow Meter
Model Approved Food/ sanitary Grade Flow MeterModel Approved Food/ sanitary Grade Flow Meter
Model Approved Food/ sanitary Grade Flow MeterManasMicrosystems
 
Pointers and Array, pointer and String.pptx
Pointers and Array, pointer and String.pptxPointers and Array, pointer and String.pptx
Pointers and Array, pointer and String.pptxAnanthi Palanisamy
 
Lesson2 Stoichiometry and mass balance.pdf
Lesson2 Stoichiometry and mass balance.pdfLesson2 Stoichiometry and mass balance.pdf
Lesson2 Stoichiometry and mass balance.pdff1002753214
 
Nexus - Final Day 12th February 2024.pptx
Nexus - Final Day 12th February 2024.pptxNexus - Final Day 12th February 2024.pptx
Nexus - Final Day 12th February 2024.pptxRohanAgarwal340656
 
Paper Machine Troubleshooting manual for paper makers
Paper Machine Troubleshooting manual for paper makersPaper Machine Troubleshooting manual for paper makers
Paper Machine Troubleshooting manual for paper makersNomanKhan691800
 
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERS
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERSCCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERS
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERSTamil949112
 
HB Self-Body characteristics UHV understanding
HB Self-Body characteristics UHV understandingHB Self-Body characteristics UHV understanding
HB Self-Body characteristics UHV understandingLeoRaju4
 
CDE_Sustainability Performance_20240214.pdf
CDE_Sustainability Performance_20240214.pdfCDE_Sustainability Performance_20240214.pdf
CDE_Sustainability Performance_20240214.pdf8-koi
 
Introduction to Binary Tree and Conersion of General tree to Binary Tree
Introduction to Binary Tree  and Conersion of General tree to Binary TreeIntroduction to Binary Tree  and Conersion of General tree to Binary Tree
Introduction to Binary Tree and Conersion of General tree to Binary TreeSwarupaDeshpande4
 
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHI
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHIINTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHI
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHIKiranKandhro1
 
chap. 3. lipid deterioration oil and fat processign
chap. 3. lipid deterioration oil and fat processignchap. 3. lipid deterioration oil and fat processign
chap. 3. lipid deterioration oil and fat processignteddymebratie
 
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdfDr. Shivashankar
 
Chapter 1 - Drilling Fluid Functions GR.ppt
Chapter 1 - Drilling Fluid Functions GR.pptChapter 1 - Drilling Fluid Functions GR.ppt
Chapter 1 - Drilling Fluid Functions GR.pptzeidali3
 
Beyond Words: Journey into Large Language Models(LLMs) - Day-1
Beyond Words: Journey into Large Language Models(LLMs) - Day-1Beyond Words: Journey into Large Language Models(LLMs) - Day-1
Beyond Words: Journey into Large Language Models(LLMs) - Day-1SahithiGurlinka
 
Microstructure of Hadfield Steels (Robert Hadfield)
Microstructure of Hadfield Steels (Robert Hadfield)Microstructure of Hadfield Steels (Robert Hadfield)
Microstructure of Hadfield Steels (Robert Hadfield)MANICKAVASAHAM G
 

Recently uploaded (20)

MedTech R&D - Tamer Emara - resume @2024
MedTech R&D - Tamer Emara - resume @2024MedTech R&D - Tamer Emara - resume @2024
MedTech R&D - Tamer Emara - resume @2024
 
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...
Introduction to Machine Learning Unit-1 Notes for II-II Mechanical Engineerin...
 
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdf
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdfForged Fitting Socket Welding Standard- ASME-B16.11-2001.pdf
Forged Fitting Socket Welding Standard- ASME-B16.11-2001.pdf
 
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh Rajput
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh RajputBasic Instrumentation Symbols | P&ID | PFD | Gaurav Singh Rajput
Basic Instrumentation Symbols | P&ID | PFD | Gaurav Singh Rajput
 
python presentation lists,strings,operation
python presentation lists,strings,operationpython presentation lists,strings,operation
python presentation lists,strings,operation
 
Model Approved Food/ sanitary Grade Flow Meter
Model Approved Food/ sanitary Grade Flow MeterModel Approved Food/ sanitary Grade Flow Meter
Model Approved Food/ sanitary Grade Flow Meter
 
Pointers and Array, pointer and String.pptx
Pointers and Array, pointer and String.pptxPointers and Array, pointer and String.pptx
Pointers and Array, pointer and String.pptx
 
Lesson2 Stoichiometry and mass balance.pdf
Lesson2 Stoichiometry and mass balance.pdfLesson2 Stoichiometry and mass balance.pdf
Lesson2 Stoichiometry and mass balance.pdf
 
Nexus - Final Day 12th February 2024.pptx
Nexus - Final Day 12th February 2024.pptxNexus - Final Day 12th February 2024.pptx
Nexus - Final Day 12th February 2024.pptx
 
Paper Machine Troubleshooting manual for paper makers
Paper Machine Troubleshooting manual for paper makersPaper Machine Troubleshooting manual for paper makers
Paper Machine Troubleshooting manual for paper makers
 
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERS
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERSCCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERS
CCW332-DIGITAL MARKETING QUESTION BANK WITH ANSWERS
 
HB Self-Body characteristics UHV understanding
HB Self-Body characteristics UHV understandingHB Self-Body characteristics UHV understanding
HB Self-Body characteristics UHV understanding
 
CDE_Sustainability Performance_20240214.pdf
CDE_Sustainability Performance_20240214.pdfCDE_Sustainability Performance_20240214.pdf
CDE_Sustainability Performance_20240214.pdf
 
Introduction to Binary Tree and Conersion of General tree to Binary Tree
Introduction to Binary Tree  and Conersion of General tree to Binary TreeIntroduction to Binary Tree  and Conersion of General tree to Binary Tree
Introduction to Binary Tree and Conersion of General tree to Binary Tree
 
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHI
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHIINTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHI
INTERACTIVE AQUATIC MUSEUM AT BAGH IBN QASIM CLIFTON KARACHI
 
chap. 3. lipid deterioration oil and fat processign
chap. 3. lipid deterioration oil and fat processignchap. 3. lipid deterioration oil and fat processign
chap. 3. lipid deterioration oil and fat processign
 
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf
21 SCHEME_21EC53_VTU_MODULE-4_COMPUTER COMMUNCATION NETWORK.pdf
 
Chapter 1 - Drilling Fluid Functions GR.ppt
Chapter 1 - Drilling Fluid Functions GR.pptChapter 1 - Drilling Fluid Functions GR.ppt
Chapter 1 - Drilling Fluid Functions GR.ppt
 
Beyond Words: Journey into Large Language Models(LLMs) - Day-1
Beyond Words: Journey into Large Language Models(LLMs) - Day-1Beyond Words: Journey into Large Language Models(LLMs) - Day-1
Beyond Words: Journey into Large Language Models(LLMs) - Day-1
 
Microstructure of Hadfield Steels (Robert Hadfield)
Microstructure of Hadfield Steels (Robert Hadfield)Microstructure of Hadfield Steels (Robert Hadfield)
Microstructure of Hadfield Steels (Robert Hadfield)
 

Continuously Updating Query Results over Real-Time Linked Data

  • 1. Ruben Taelman - @rubensworks iMinds - Ghent University Continuously Updating Query Results over Real-Time Linked Data
  • 2. Dynamic Linked Data E.g. Thermometer measures every minute: “19,05°C” - 30-05-2016 11:00 “19,06°C” - 30-05-2016 11:01 “19,11°C” - 30-05-2016 11:02 “19,08°C” - 30-05-2016 11:03 … Typically exposed as an RDF stream = stream of <RDF triple, timestamp>
  • 3. Querying continous data Clients send queries to server: e.g. What is the current temperature? Server continuously evaluates the queries → Server does all of the work Cause of low public endpoint availability! ½ have availability of < 95% (Buil-Aranda 2013) → Clients just wait for results
  • 4. What if we moved continuous query evaluation to the client? → to lower server load
  • 5. Triple Pattern Fragments does this for static data! Triple pattern fragments (TPF) (Verborgh 2016): Servers can only respond to triple pattern queries Clients need to evaluate queries locally → Lowers server complexity Can we do the same for dynamic data?
  • 6. Overview Dynamic data representation Query streamer engine Evaluation
  • 7. Overview Dynamic data representation Query streamer engine Evaluation
  • 8. Dynamic data representation Expose dynamic data through the TPF interface → Represent dynamic data in RDF We annotate dynamic data with the time at which they are valid → Client can derive the time at which data can change! But how do we annotate data/triples with time?
  • 9. Annotation methods Reification Singleton properties (Nguyen 2014) Graphs Implicit graphs Outdated Instantiate predicates Define fourth element in quad TPF makes triples (de)referencable
  • 10. Time labeling types Time interval Expiration time Start- and endtime of validity Good for maintaining a history of elements Endtime of validity When only the latest version is required
  • 11. Dynamic data example radio:bbc-radio-1 m:plays radio:jauz-netsky-higher. GRAPH _:g1 { radio:bbc-radio-1 m:plays radio:jauz-netsky-higher. } _:g1 tmp:interval _:interval_1. _:interval_1 tmp:initial "2016-05-30T09:15:00"^^xsd:dateTime. _:interval_1 tmp:final "2016-05-30T09:20:00"^^xsd:dateTime. Graph-annotation: [ 9:15, 9:20 ]
  • 14. Overview Dynamic data representation Query streamer engine Evaluation
  • 15. Measure query execution times for query duration Query: “All trains with their delay in station X within the next hour” Frequency: 10 seconds Clients: 1 Engine: Query streamer Annotation methods: singleton property, graph, implicit graph Time labeling types: time interval, expiration time Evaluating annotation methods
  • 16. Evaluating annotation methods Time interval Expiration time
  • 17. Evaluating scalability Measure server CPU usage for increasing # clients Query: “All trains with their delay in station X within the next hour” Frequency: 10 seconds Clients: 1 → 200 Engines: Query streamer, C-SPARQL (Barbieri 2012) and CQELS (Le-Phuoc 2011) Annotation method: graph Time labeling types: expiration time
  • 18. Query Streamer has better scalability
  • 19. Query Streamer moves load from server to client
  • 20. Overview Dynamic data representation Annotate dynamic data with time Query streamer engine Client-side query engine Dynamic data at TPF server Evaluation Annotation methods Scalability
  • 21. Conclusions Further evaluation: Different query types, …? Solve efficiency-problem time intervals? Promising approach for improved scalability