Presentation for the MEPDaW workshop @ESWC 2016

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Outdated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 ]

12. Overview
Dynamic data representation
Query engine
Evaluation

13. Query streamer engine

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