A proof-of-concept implementation for enabling continuously updating queries using Triple Pattern Fragments. This is an extra layer on top of the existing Triple Pattern Fragments Client
The TPF endpoint must have time-annotations for all dynamic triples using some annotation method. The client can accept a regular SPARQL query, and it will detect any dynamic triple patterns and will stream its results.
ESWC 2016 paper with more details about this concept.
Install the latest version from GitHub:
$ git clone [email protected]:rubensworks/TPFStreamingQueryExecutor
$ cd TPFStreamingQueryExecutor
$ npm install
This will also install the custom Client.js, Server.js and N3 forks.
A docker container can be started like this:
docker build -t tpfqs-client . && docker run \
-e "QUERY=<SPARQL query>" \
-e "TARGET=http://<server>:3000/<datasource>" \
-e "CACHING=true" \
-e "INTERVAL=true" \
-e "DEBUG=true" \
--rm tpfqs-client graphs
To start the train departure information demo, run:
cd bin && ./startLiveTrainServer.sh
To start the live music demo from Q-Music, run:
cd bin && ./startLiveMusicServer.sh
To be able to run custom queries, the TPF server will require a dataset that contains time annotations for the dynamic triples using some type of RDF annotation.
It is advised to read through the live-ldf-server script for an example on how to annotate triples on-the-fly and storing them at some TPF endpoint.
The querytrain [annotationtype] script shows how to call the actual StreamingClient.