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

6. Continuously appended data

7. Potentially infinite

8. Time-stamped tuples

9. Continuous queries

10. Latest used in queries

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?

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

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

31. AEMET Linked Data 18

32. JSI Sensors 19

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

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

40. Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/

41. Ontology: http://purl.oclc.org/NET/ssnx/ssn

42. A good number of internal and external references to SSN Ontology

43. http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography

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"

60. Mappingexpression overstreamingsources conjunctive query

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

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

79. SPARQL-STR + GSN

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)