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Ingredients for the Semantic Sensor Web Jožef Stefan Institute Ljubljana, Slovenia September 23rd 2011 Oscar Corcho Facultad de Informática,Universidad Politécnica de Madrid Campus de Montegancedosn, 28660 Boadilla del Monte, Madrid http://www.oeg-upm.net ocorcho@fi.upm.es Phone: 34.91.3366605 Fax: 34.91.3524819
Index PART I. Motivation From Sensor Networks…           … to the Sensor Web / Internet of Things…  			   … to Semantic Sensor Web and       Linked Stream/Sensor Data
Sensor Networks Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources Source: Antonis Deligiannakis
Anexample: SmartCities 4 Environmentalsensor nodes Parking sensor nodes Santander
Sensor Networks and Streaming Data 5 ,[object Object]
 Continuously appended data
 Potentially infinite
 Time-stamped tuples
 Continuous queries
 Latest used in queries
 Time and tuple-based windows(t9, a1, a2, ... , an) (t8, a1, a2, ... , an) (t7, a1, a2, ... , an) ... ... (t1, a1, a2, ... , an) ... ... Window [t7 - t9] Streaming Data ,[object Object]
 Cheap, Noisy, Unreliable (depends)
 Low computational, power resources, storage
 Distributed query execution
 Routing, OptimizationQuery EnablingSemanticIntegration of Streaming Data Sources
Who are theendusers of sensor networks? Theclimatechangeexpert, or a simple citizen Source: Dave de Roure
Notonlyenvironmentalsensors, butmanyothers… 7 Weather Sensors Sensor Dataset GPS Sensors Satellite Sensors Camera Sensors Source: H Patni, C Henson, A Sheth
How do wemakethesesensors more accessible?  8 Source: SemsorGrid4Env consortium
The Sensor Web (relatedto Internet of Things) Universal, web-based access to sensor data Some sensor networkproperties: Networked Mostlywireless Each network with some kind of authority and administration Sometimes noisy 9 Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009
Should we care as computer scientists? They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines] Hence interesting for those computer scientists interested on helping these users… We are many ;-) But they are also interesting for “pure” computer scientists They address an important set of “grand challenge” Computer Science issues including:  Heterogeneity Scale Scalability Autonomic behaviour Persistence, evolution Deployment challenges Mobility Source: Dave de Roure
A semanticperspectiveonthesechallenges Sensor data querying and (pre-)processing Data heterogeneity Data quality New inferencecapabilitiesrequiredtodealwith sensor information Sensor data modelrepresentation and management For data publication, integration and discovery Bridgingbetween sensor data and ontologicalrepresentationsfor data integration Ontologies: Observations and measurements, time series, etc. Eventmodels Userinteractionwith sensor data
Vision (aftersomeiterations, and more to come) 12 RWI WorkingGrouponIoT: NetworkedKnowledgeGluhak et al, 2011. AnArchitecturalBlueprintfor a Real-World Internet', FutureInternet Assembly
Semantic Sensor Web / LinkedStream-Sensor Data (LSD) A representation of sensor/streamdata followingthestandards of LinkedData ButwhatisLinked Data?
WhatisLinked Data? 14 ,[object Object]
… where data are given well-defined and explicitly represented meaning, …
… so that it can be shared and used by humans and machines, ...
... better enabling them to work in cooperation
And clear principles on how to publish data,[object Object]
Semantic Sensor Web / LinkedStream-Sensor Data (LSD) A representation of sensor/streamdata followingthestandards of LinkedData Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data source Usingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections Earlyreferences… AmitSheth, CoryHenson, and SatyaSahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83 Sequeda J, Corcho O. LinkedStream Data: A Position Paper. Proceedingsof the 2nd International WorkshoponSemantic Sensor Networks, SSN 09 Le-Phuoc D, Parreira JX, Hauswirth M. Challengesin LinkedStream Data Processing: A Position Paper. Proceedingsof the3rd International WorkshoponSemantic Sensor Networks, SSN 10
Let’schecksomeexamples Meteorological data in Spain: automaticweatherstations http://aemet.linkeddata.es/ Paperunder open review at theSemantic Web Journal http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data Live sensors in Slovenia http://sensors.ijs.si/ ChannelCoastalObservatory in Southern UK http://webgis1.geodata.soton.ac.uk/flood.html And some more from DERI Galway, Knoesis, CSIRO, etc. 17
AEMET Linked Data 18
JSI Sensors 19
Coastal Channel Observatory and other sources 20 Sensors, Mappings and Queries Work with Flood environmental sensor data. SemSorGrid4Env project www.semsorgrid4env.eu.
PART II How to create, publish and consume Linked Stream Data
HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
[object Object]
State of the art on sensor network ontologies in the report below
In 2009, a W3C incubator group was started, which has just finished
Lots of good people there
Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
Ontology: http://purl.oclc.org/NET/ssnx/ssn
A good number of internal and external references to SSN Ontology
http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography
SSN Ontology paper submitted to Journal of Web SemanticsSSN ontologies. History
Deployment System OperatingRestriction Process Device PlatformSite Data Skeleton ConstraintBlock MeasuringCapability Overview of the SSN ontology modules
deploymentProcesPart only Deployment System OperatingRestriction hasSubsystem only, some hasSurvivalRange only SurvivalRange DeploymentRelatedProcess hasDeployment only System OperatingRange Deployment hasOperatingRange only deployedSystem only deployedOnPlatform only Process hasInput only inDeployment only Device Input Device Process onPlatform only PlatformSite Output Platform hasOutput only, some attachedSystem only Data Skeleton implements some isProducedBy some Sensor Sensing hasValue some SensorOutput sensingMethodUsed only detects only SensingDevice observes only SensorInput ObservationValue isProxyFor only Property isPropertyOf some includesEvent some observedProperty only observationResult only hasProperty only, some observedBy only Observation FeatureOfInterest featureOfInterest only ConstraintBlock MeasuringCapability hasMeasurementCapability only forProperty only inCondition only inCondition only Condition MeasurementCapability Overview of the SSN ontologies
SSN Ontology. Sensor and environmental properties Skeleton Property Communication MeasuringCapability hasMeasurementProperty only MeasurementCapability MeasurementProperty Accuracy Frequency Precision Resolution Selectivity Latency DetectionLimit Drift MeasurementRange ResponseTime Sensitivity EnergyRestriction OperatingRestriction hasOperatingProperty only OperatingProperty OperatingRange EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange hasSurvivalProperty only SurvivalRange SurvivalProperty EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime
A usageexample Upper SWEET DOLCE UltraLite SSG4Env  infrastructure SSN Schema Service External OrdnanceSurvey FOAF Flood domain CoastalDefences AdditionalRegions Role 27
AEMET Ontology Network 83 classes 102 objectproperties 80 datatypeproperties 19 instances SROIQ(D)
HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
Goodpractices in URI Definition Sorry, no clearpracticesyet…
Goodpractices in URI Definition Wehavetoidentify… Sensors Features of interest Properties Observations Debate betweenbeingobservationor sensor-centric Observation-centricseemsto be thewinner Forsomedetails of sensor-centric, check [Sequeda and Corcho, 2009]
HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
Queries to Sensor/Stream Data SNEEql RSTREAM SELECT id, speed, direction  FROM wind[NOW]; Streaming SPARQL PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#> SELECT ?sensor ?speed ?direction FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS WHERE {     ?sensor a fire:WindSensor; fire:hasMeasurements ?WindSpeed, ?WindDirection.     ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeedValue ?speed; fire:hasTimestampValue ?wsTime.     ?WindDirection a fire:WindDirectionMeasurement; fire:hasDirectionValue ?direction; fire:hasTimestampValue ?dirTime.     FILTER (?wsTime == ?dirTime) } C-SPARQL REGISTER QUERY WindSpeedAndDirection AS PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#> SELECT ?sensor ?speed ?direction FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC] WHERE { … 33 Semantically Integrating Streaming and Stored Data
SPARQL-STR v1 34 Sensors, Mappings and Queries SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>  [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE {    ?WaveObs a 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 Source: 	EnablingOntology-based Access toStreaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010
SPARQL-STR v2 SPARQLStream  algebra(S1 S2 Sm)  GSN Query translation q SNEEql, GSN API Sensor Network (S1) SPARQLStream (Og)  Relational DB (S2) Query Evaluator Stream-to-Ontology Mappings (R2RML) Client Stream Engine (S3) RDF Store (Sm) Data translation [tuples] [triples] Ontology-based Streaming Data Access Service Source: 	PlanetDatadeliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu
CreatingMappings 36 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
R2RML RDB2RDF W3C Group, R2RML Mappinglanguage: http://www.w3.org/2001/sw/rdb2rdf/r2rml/ 37 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"

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Ingredients for Semantic Sensor Networks

  • 1. Ingredients for the Semantic Sensor Web Jožef Stefan Institute Ljubljana, Slovenia September 23rd 2011 Oscar Corcho Facultad de Informática,Universidad Politécnica de Madrid Campus de Montegancedosn, 28660 Boadilla del Monte, Madrid http://www.oeg-upm.net ocorcho@fi.upm.es Phone: 34.91.3366605 Fax: 34.91.3524819
  • 2. Index PART I. Motivation From Sensor Networks… … to the Sensor Web / Internet of Things… … to Semantic Sensor Web and Linked Stream/Sensor Data
  • 3. Sensor Networks Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources Source: Antonis Deligiannakis
  • 4. Anexample: SmartCities 4 Environmentalsensor nodes Parking sensor nodes Santander
  • 5.
  • 10. Latest used in queries
  • 11.
  • 12. Cheap, Noisy, Unreliable (depends)
  • 13. Low computational, power resources, storage
  • 14. Distributed query execution
  • 15. Routing, OptimizationQuery EnablingSemanticIntegration of Streaming Data Sources
  • 16. Who are theendusers of sensor networks? Theclimatechangeexpert, or a simple citizen Source: Dave de Roure
  • 17. Notonlyenvironmentalsensors, butmanyothers… 7 Weather Sensors Sensor Dataset GPS Sensors Satellite Sensors Camera Sensors Source: H Patni, C Henson, A Sheth
  • 18. How do wemakethesesensors more accessible? 8 Source: SemsorGrid4Env consortium
  • 19. The Sensor Web (relatedto Internet of Things) Universal, web-based access to sensor data Some sensor networkproperties: Networked Mostlywireless Each network with some kind of authority and administration Sometimes noisy 9 Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009
  • 20. Should we care as computer scientists? They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines] Hence interesting for those computer scientists interested on helping these users… We are many ;-) But they are also interesting for “pure” computer scientists They address an important set of “grand challenge” Computer Science issues including: Heterogeneity Scale Scalability Autonomic behaviour Persistence, evolution Deployment challenges Mobility Source: Dave de Roure
  • 21. A semanticperspectiveonthesechallenges Sensor data querying and (pre-)processing Data heterogeneity Data quality New inferencecapabilitiesrequiredtodealwith sensor information Sensor data modelrepresentation and management For data publication, integration and discovery Bridgingbetween sensor data and ontologicalrepresentationsfor data integration Ontologies: Observations and measurements, time series, etc. Eventmodels Userinteractionwith sensor data
  • 22. Vision (aftersomeiterations, and more to come) 12 RWI WorkingGrouponIoT: NetworkedKnowledgeGluhak et al, 2011. AnArchitecturalBlueprintfor a Real-World Internet', FutureInternet Assembly
  • 23. Semantic Sensor Web / LinkedStream-Sensor Data (LSD) A representation of sensor/streamdata followingthestandards of LinkedData ButwhatisLinked Data?
  • 24.
  • 25. … where data are given well-defined and explicitly represented meaning, …
  • 26. … so that it can be shared and used by humans and machines, ...
  • 27. ... better enabling them to work in cooperation
  • 28.
  • 29. Semantic Sensor Web / LinkedStream-Sensor Data (LSD) A representation of sensor/streamdata followingthestandards of LinkedData Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data source Usingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections Earlyreferences… AmitSheth, CoryHenson, and SatyaSahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83 Sequeda J, Corcho O. LinkedStream Data: A Position Paper. Proceedingsof the 2nd International WorkshoponSemantic Sensor Networks, SSN 09 Le-Phuoc D, Parreira JX, Hauswirth M. Challengesin LinkedStream Data Processing: A Position Paper. Proceedingsof the3rd International WorkshoponSemantic Sensor Networks, SSN 10
  • 30. Let’schecksomeexamples Meteorological data in Spain: automaticweatherstations http://aemet.linkeddata.es/ Paperunder open review at theSemantic Web Journal http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data Live sensors in Slovenia http://sensors.ijs.si/ ChannelCoastalObservatory in Southern UK http://webgis1.geodata.soton.ac.uk/flood.html And some more from DERI Galway, Knoesis, CSIRO, etc. 17
  • 33. Coastal Channel Observatory and other sources 20 Sensors, Mappings and Queries Work with Flood environmental sensor data. SemSorGrid4Env project www.semsorgrid4env.eu.
  • 34. PART II How to create, publish and consume Linked Stream Data
  • 35. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 36.
  • 37. State of the art on sensor network ontologies in the report below
  • 38. In 2009, a W3C incubator group was started, which has just finished
  • 39. Lots of good people there
  • 42. A good number of internal and external references to SSN Ontology
  • 44. SSN Ontology paper submitted to Journal of Web SemanticsSSN ontologies. History
  • 45. Deployment System OperatingRestriction Process Device PlatformSite Data Skeleton ConstraintBlock MeasuringCapability Overview of the SSN ontology modules
  • 46. deploymentProcesPart only Deployment System OperatingRestriction hasSubsystem only, some hasSurvivalRange only SurvivalRange DeploymentRelatedProcess hasDeployment only System OperatingRange Deployment hasOperatingRange only deployedSystem only deployedOnPlatform only Process hasInput only inDeployment only Device Input Device Process onPlatform only PlatformSite Output Platform hasOutput only, some attachedSystem only Data Skeleton implements some isProducedBy some Sensor Sensing hasValue some SensorOutput sensingMethodUsed only detects only SensingDevice observes only SensorInput ObservationValue isProxyFor only Property isPropertyOf some includesEvent some observedProperty only observationResult only hasProperty only, some observedBy only Observation FeatureOfInterest featureOfInterest only ConstraintBlock MeasuringCapability hasMeasurementCapability only forProperty only inCondition only inCondition only Condition MeasurementCapability Overview of the SSN ontologies
  • 47. SSN Ontology. Sensor and environmental properties Skeleton Property Communication MeasuringCapability hasMeasurementProperty only MeasurementCapability MeasurementProperty Accuracy Frequency Precision Resolution Selectivity Latency DetectionLimit Drift MeasurementRange ResponseTime Sensitivity EnergyRestriction OperatingRestriction hasOperatingProperty only OperatingProperty OperatingRange EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange hasSurvivalProperty only SurvivalRange SurvivalProperty EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime
  • 48. A usageexample Upper SWEET DOLCE UltraLite SSG4Env infrastructure SSN Schema Service External OrdnanceSurvey FOAF Flood domain CoastalDefences AdditionalRegions Role 27
  • 49. AEMET Ontology Network 83 classes 102 objectproperties 80 datatypeproperties 19 instances SROIQ(D)
  • 50. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 51. Goodpractices in URI Definition Sorry, no clearpracticesyet…
  • 52. Goodpractices in URI Definition Wehavetoidentify… Sensors Features of interest Properties Observations Debate betweenbeingobservationor sensor-centric Observation-centricseemsto be thewinner Forsomedetails of sensor-centric, check [Sequeda and Corcho, 2009]
  • 53. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 54. Queries to Sensor/Stream Data SNEEql RSTREAM SELECT id, speed, direction FROM wind[NOW]; Streaming SPARQL PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#> SELECT ?sensor ?speed ?direction FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS WHERE { ?sensor a fire:WindSensor; fire:hasMeasurements ?WindSpeed, ?WindDirection. ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeedValue ?speed; fire:hasTimestampValue ?wsTime. ?WindDirection a fire:WindDirectionMeasurement; fire:hasDirectionValue ?direction; fire:hasTimestampValue ?dirTime. FILTER (?wsTime == ?dirTime) } C-SPARQL REGISTER QUERY WindSpeedAndDirection AS PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#> SELECT ?sensor ?speed ?direction FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC] WHERE { … 33 Semantically Integrating Streaming and Stored Data
  • 55. SPARQL-STR v1 34 Sensors, Mappings and Queries SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE { ?WaveObs a 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 Source: EnablingOntology-based Access toStreaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010
  • 56. SPARQL-STR v2 SPARQLStream algebra(S1 S2 Sm) GSN Query translation q SNEEql, GSN API Sensor Network (S1) SPARQLStream (Og) Relational DB (S2) Query Evaluator Stream-to-Ontology Mappings (R2RML) Client Stream Engine (S3) RDF Store (Sm) Data translation [tuples] [triples] Ontology-based Streaming Data Access Service Source: PlanetDatadeliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu
  • 57. CreatingMappings 36 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
  • 58. R2RML RDB2RDF W3C Group, R2RML Mappinglanguage: http://www.w3.org/2001/sw/rdb2rdf/r2rml/ 37 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"
  • 59.
  • 61. Algebra expressions transformed to GSN API 39 Sensors, Mappings and Queries π SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 timed, sp_wind σ sp_wind>10 ω 5 Hour 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 wan7
  • 62. Algebra construction 40 Sensors, Mappings and Queries π timed, sp_wind windsensor1 σ windsensor2 sp_wind>10 ω 5 Hour wan7
  • 63. Staticoptimization 41 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
  • 64. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 65.
  • 66. Temporal/spatial data are represented by linear constraints, representing as literals of type strdf:semiLinearPointSet.
  • 67. OGC simple feature geometries (points, polylines, polygons etc.) using the Well-known Text representationfloodInstances:ModelledFloodIngressDataset rdf:typeinfo:Dataset; rdfs:label "Modelled flood ingress Dataset" ; time:hasTemporalExtent "[NOW,NOW+12]"^^RegistryOntology:TemporalInterval; Services:coversRegionAdditionalRegions:CoastalDefencePartnershipModelledArea; Services:includesFeatureTypeCoastalDefences:FloodPlain ; Services:includesPropertyTypeCoastalDefences:WaterDepth. AdditionalRegions:CoastalDefencePartnershipModelledArea rdf:typespace:Region; Services:hasSpatialExtent "POLYGON((625145.2823357487 5624227.2548582135, 625145.2823357487 5637255.203057151, 647383.6564885917 5637255.203057151, 647383.6564885917 5624227.2548582135, 625145.2823357487 5624227.2548582135))"^^RegistryOntology:WKT. Source: Our NKUA partners at SemsorGrid4Env 2nd Year Review Meeting - Brussels, 16-17 Nov. 2010 43
  • 68. Querying: stSPARQL Find all WMS services with FOI flood plain that cover the Coastal Defence Partnership modelled area and provide valid information for the next 12 hours select distinct ?ENDPOINT where { ?SERVICE rdf:typeServices:WebService . ?SERVICE Services:hasEndpointReference ?ENDPOINT . ?SERVICE Services:hasServiceTypeServices:WMS . ?SERVICE Services:hasDataset ?DATASET . ?DATASET Services:includesFeatureTypeCoastalDefences:FloodPlain. ?DATASET time:hasTemporalExtent ?TIME . filter(?TIME contains “[NOW,NOW+12]"^^RegistryOntology:TemporalInterval) . ?DATASET Services:coversRegion ?SERVICEREGION . ?SERVICEREGION Services:hasSpatialExtent ?SERVICEREGIONGEO . AdditionalRegions:CoastalDefencePartnershipModelledArea Services:hasSpatialExtent ?COSTALGEO . filter(?SERVICEREGIONGEO contains ?COSTALGEO) } Source: Our NKUA partners at SemsorGrid4Env 2nd Year Review Meeting - Brussels, 16-17 Nov. 2010 44
  • 69. Implementation: STRABON SupportforstRDF and SPARQL, plus Topologicaloperators in spatialfilters DISJOINT, TOUCH, EQUALS, CONTAINS, COVERS, COVERED BY, OVERLAP ConstructSpatialGeometries e.g. ?geo1 union ?geo2 Projectionoperation e.g. ?geo[1,2] Renameoperator ConversionFunctionsforexportinggeometries: e.g. ToWKT(?geo) AS ?geoAsWKT Library thatreturns SPARQL results as a KML document 45 Source: Our NKUA partners at SemsorGrid4Env
  • 70. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 71. Sensor High-level API Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • 72. Sensor High-level API Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • 73. API definition Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env
  • 74. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 75. SwissEx 51 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
  • 76. 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 52 Sensors, Mappings and Queries
  • 77. SensorMetadata 53 Sensors, Mappings and Queries station location sensors model properties
  • 78. Sensor Data: Observations 54 Sensors, Mappings and Queries Heterogeneity Integration
  • 80. HowtodealwithLinkedStream/Sensor Data Ingredients Anontologymodel Goodpractices in URI definition Supportingsemantictechnology SPARQL extensions Tohandle time and tuplewindows Tohandlespatio-temporal constraints REST APIstoaccessit Anotherexample: semanticallyenriching GSN A couple of lessonslearned
  • 81. LessonsLearned High-level (part I) Sensor data isyetanothergoodsource of data withsomespecialproperties Everythingthatwe do withourrelationaldatasetsorother data sources can be done with sensor data Practicallessonslearned (part II) Manageseparatelydata and metadata of thesensors Data shouldalways be separatedbetweenrealtime-data and historical-data Use the time formatxsd:dateTimeand the time zone Graphicalrepresentation of data forweeksormonthsisnot trivial anyway
  • 82. Ingredients for the Semantic Sensor Web Jožef Stefan Institute Ljubljana, Slovenia September 23rd 2011 Oscar Corcho Acknowledgments: allthoseidentified in slides + the SemsorGrid4Env team (Jean Paul Calbimonte, Alasdair Gray, Kevin Page, etc.), the AEMET team at OEG-UPM (GhislainAtemezing, Daniel Garijo, José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET)

Editor's Notes

  1. The where clasue for both SPARQL extensions is the same