1. Date: 06/07/2011 OEG – Charlas de Grupo Sensors, Mappings and Queries(from a Semantic Web perspective) Jean-Paul Calbimonte Ontology Engineering Group. Facultad de Informática, Universidad Politécnica de Madrid. jp.calbimonte@upm.es

2. Outline 2 Sensors, Mappings and Queries Previouswork Sensor data Sensormetadata Semantic Web representation Mappingsensorstreams Query observations Data integration

3. Previously on … 3 Sensors, Mappings and Queries Work with Flood environmental sensor data. SemSorGrid4Env project www.semsorgrid4env.eu. Live sensor data Continuousqueries We are gettinghighwavesaroundhere!

4. Previously on… 4 Sensors, Mappings and Queries SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE { ?WaveObsa sea:WaveHeightObservation; sea:hasValue ?waveheight; } SELECT measuredFROM wavesamples [NOW -10 MIN] conceptmap-def WaveHeightMeasurement virtualStream <http://ssg4env.eu/Readings.srdf> uri-as concat('ssg4env:WaveSM_', wavesamples.sensorid,wavesamples.ts) attributemap-defhasValue operation constant has-columnwavesamples.measured dbrelationmap-def isProducedBy toConcept Sensor joins-via condition equals has-column sensors.sensorid has-columnwavesamples.sensorid conceptmap-def Sensor uri-as concat('ssg4env:Sensor_',sensors.sensorid) attributemap-def hasSensorid operation constant has-column sensors.sensorid Query translation SNEEql SPARQLStream Query Processing Stream-to-Ontology mappings Client Sensor Network Data translation [tuples] [triples] S2O Mappings

5. Previously on … 5 Sensors, Mappings and Queries Mapping data streams to ontologies Use ontological schemas to write queries over streaming data sources Rewriting SPARQL-Stream queries into declarative stream queries (e.g. SNEEql) conceptmap-def WindSpeedMeasurement virtualStream <http://ssg4env.eu/Readings.srdf> uri-as concat('ssg4env:WindSM_', windsamples.sensorid,windsamples.ts) attributemap-def hasSpeed operation constant has-column windsamples.speed dbrelationmap-def isProducedBy toConcept Sensor joins-via condition equals has-column sensors.sensorid has-column windsamples.sensorid conceptmap-def Sensor uri-as concat('ssg4env:Sensor_',sensors.sensorid) attributemap-def hasSensorid operation constant has-column sensors.sensorid WindSpeed Measurement s:windsamples hasSpeed sensorid: int PK ts: datetime PK speed: float isProducedBy xsd:float Sensor t:sensors hasSensorid sensorid: int PK sensorname: st xsd:int Ontologies Streams S2O Mapping Calbimonte, J-P., Corcho O., Gray, A. EnablingOntology-based Access to Streaming Data Sources. In ISWC 2010.

6. Keep on working Other streaming technologies? Otherquerylanguages? Hundreds of sensors? Integratingheterogeneous data? 6 Sensors, Mappings and Queries Beyond SSG4Env Streaming Data Services OtherthanSNEEql: CQL-like, Service calls Not onlytoy-likestreams Many sources fromdifferent providers, schemas

7. A little help fromourfriends FP7 Network of Excellence SwissExperiment 7 Sensors, Mappings and Queries Environmental and GeoScience research Swiss Alps Geo Researcher Snow, Wind, Radiation. Lot of stuff Real-time data ... ... ... I want data to createmyprettymodels and compare I want to win a best paperaward

8. SwissEx 8 Sensors, Mappings and Queries Global Sensor Networks, deployment for SwissEx. Distributedenvironment: GSN Davos, GSN Zurich, etc. In each site, a number of sensorsavailable Each one withdifferentschema Metadatastored in wiki Federatedmetadata management: Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T., Lehning, M.EffectiveMetadata Management in federatedSensor Networks. in SUTC, 2010 Sensor observations Sensormetadata

9. SensorMetadata 9 Sensors, Mappings and Queries station location sensors model properties

10. SensorMetadata Whatproperties are measured Whichsensorsavailable Where are theylocated How are theyconfigured Whoisresponsible 10 Sensors, Mappings and Queries

11. Sensor Data: Observations 11 Sensors, Mappings and Queries Heterogeneity Integration

12. A model to bindthem all W3C SSN Ontology 12 Sensors, Mappings and Queries ssn:isProducedBy ssn:SensorOutput ssn:Sensor ssn:observedBy ssn:observationResult ssn:hasValue ssn:Observation ssn:ObservationValue ssn:observes ssn:featureOfInterest quantityValue ssn:observedProperty ssn:FeatureOfInterest xsd:datatype ssn:Property ssn:hasProperty

13. SSN Ontology 13 Sensors, Mappings and Queries ssn:OperatingRange ssn:hasOperatingRange ssn:Deployment ssn:Sensing ssn:hasDeployment ssn:System ssn:deployedOnPlatform ssn:implements ssn:Sensor ssn:onPlatform ssn:Device ssn:Platform ssn:hasMeasurementCapability ssn:MeasurementCapability ssn:SensingDevice

14. So Far 14 Sensors, Mappings and Queries SSN Ontology Query translation q Client Target query Sensor Networks SPARQLStream q’ Query Processing mappings ??? d’ Data translation d [tuple] [tuple] [triple] Ontology-based sensor query service

15. CreatingMappings 15 Sensors, Mappings and Queries ssn:observedProperty ssn:Observation ssn:Property http://swissex.ch/data# Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed ssn:observationResult wan7 ssn:SensorOutput timed: datetime PK sp_wind: float http://swissex.ch/data# Wan7/ WindSpeed/ ObsOutput{timed} ssn:hasValue ssn:ObservationValue http://swissex.ch/data# Wan7/WindSpeed/ObsValue{timed} qudt:numericValue xsd:decimal sp_wind

16. R2RML RDB2RDF W3C Group, R2RML Mappinglanguage: http://www.w3.org/2001/sw/rdb2rdf/r2rml/ 16 Sensors, Mappings and Queries :Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:classssn:ObservationValue; rr:graphssg:swissexsnow.srdf]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicatessn:hasQuantityValue]; rr:objectMap[ rr:column"sp_wind" ] ]; . <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00> a ssn:ObservationValue <http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00> ssn:hasQuantityValue" 4.5"

17. So Far 17 Sensors, Mappings and Queries SSN Ontology Query translation ??? q Client Target query Sensor Networks SPARQLStream q’ Query Processing mappings R2RML d’ Data translation d GSN [tuple] [tuple] [triple] Ontology-based sensor query service

18. Data Access GSN Web Services GSN URL API Compose the query as a URL: 18 Sensors, Mappings and Queries http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 & field [0]= sp_wind& from =15/05/2011+05:00:00& to =15/05/2011+10:00:00& c_vs [0]= wan7 & c_field [0]= sp_wind& c_min [0]=10 ? SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10

19. Algebra expressions 19 Sensors, Mappings and Queries π http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 & field [0]= sp_wind& from =15/05/2011+05:00:00& to =15/05/2011+10:00:00& c_vs [0]= wan7 & c_field [0]= sp_wind& c_min [0]=10 timed, sp_wind σ sp_wind>10 ω 5 Hour SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 wan7

20. Algebra construction 20 Sensors, Mappings and Queries π timed, sp_wind windsensor1 σ windsensor2 sp_wind>10 ω 5 Hour wan7

21. Staticoptimization 21 Sensors, Mappings and Queries π π π timed, sp_wind timed, windvalue timed, windvalue σ σ σ sp_wind>10 windvalue>10 windvalue>10 ω ω ω 5 Hour 5 Hour 5 Hour wan7 windsensor1 windsensor2

22. Gettingthingsdone Transformed wiki metadata to SSN instances in RDF Generated R2RML mappings for all sensors Implementation of Ontology-basedquerying over GSN Fronting GSN with SPARQL-Stream queries Numbers: 28 Deployments Aprox. 50 sensors in eachdeployment More than 1500 sensors Live updates. Lowfrequency Access to all metadata/not all data 22 Sensors, Mappings and Queries

23. Uglylittledemo Problems Toomanysensors TooHeterogeneous Anysensorsavailable in thisregion? Sensorsthatmeasurewind speed? How about getting the data? 23 Sensors, Mappings and Queries

24. Playingwithit 24 Sensors, Mappings and Queries

25. Playingwithit 25 Sensors, Mappings and Queries

26. Conclusions Thou shalt use the SSN Ontology ! Using R2RML mappings Wecanapply the approach to different technologies Wecan translate to querylanguages and APIs Algebra expressions are cool Scale to hundreds of sensors 26 Sensors, Mappings and Queries

27. TODO Makethings usable Combine with LD generation Integratewithstored RDF data Streaming/Dynamic RDF vocabularies Apply to otherenvironments MonetDB OGC SOS Services Pachube 27 Sensors, Mappings and Queries

28. Thanks! Questions, please. 28 Sensors, Mappings and Queries