Ingredients for Semantic Sensor Networks

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  • The where clasue for both SPARQL extensions is the same
  • Ingredients for Semantic Sensor Networks

    1. 1. Ingredients for the Semantic Sensor Web<br />Jožef Stefan Institute<br />Ljubljana, Slovenia<br />September 23rd 2011<br />Oscar Corcho<br />Facultad de Informática,Universidad Politécnica de Madrid<br />Campus de Montegancedosn, 28660 Boadilla del Monte, Madrid<br />http://www.oeg-upm.net<br />ocorcho@fi.upm.es<br />Phone: 34.91.3366605<br />Fax: 34.91.3524819<br />
    2. 2. Index<br />PART I. Motivation<br />From Sensor Networks… … to the Sensor Web / Internet of Things… … to Semantic Sensor Web and Linked Stream/Sensor Data<br />
    3. 3. Sensor Networks<br />Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources<br />Source: Antonis Deligiannakis<br />
    4. 4. Anexample: SmartCities<br />4<br />Environmentalsensor nodes<br />Parking sensor nodes<br />Santander<br />
    5. 5. Sensor Networks and Streaming Data<br />5<br /><ul><li>Streaming Data
    6. 6. Continuously appended data
    7. 7. Potentially infinite
    8. 8. Time-stamped tuples
    9. 9. Continuous queries
    10. 10. Latest used in queries
    11. 11. Time and tuple-based windows</li></ul>(t9, a1, a2, ... , an)<br />(t8, a1, a2, ... , an)<br />(t7, a1, a2, ... , an)<br />...<br />...<br />(t1, a1, a2, ... , an)<br />...<br />...<br />Window [t7 - t9]<br />Streaming Data<br /><ul><li>Sensor Networks
    12. 12. Cheap, Noisy, Unreliable (depends)
    13. 13. Low computational, power resources, storage
    14. 14. Distributed query execution
    15. 15. Routing, Optimization</li></ul>Query<br />EnablingSemanticIntegration of Streaming Data Sources<br />
    16. 16. Who are theendusers of sensor networks?<br />Theclimatechangeexpert, or a simple citizen<br />Source: Dave de Roure<br />
    17. 17. Notonlyenvironmentalsensors, butmanyothers…<br />7<br />Weather Sensors<br />Sensor Dataset<br />GPS Sensors<br />Satellite Sensors<br />Camera Sensors<br />Source: H Patni, C Henson, A Sheth<br />
    18. 18. How do wemakethesesensors more accessible? <br />8<br />Source: SemsorGrid4Env consortium<br />
    19. 19. The Sensor Web (relatedto Internet of Things)<br />Universal, web-based access to sensor data<br />Some sensor networkproperties:<br />Networked<br />Mostlywireless<br />Each network with some kind of authority and administration<br />Sometimes noisy<br />9<br />Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009<br />
    20. 20. Should we care as computer scientists?<br />They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines]<br />Hence interesting for those computer scientists interested on helping these users… We are many ;-)<br />But they are also interesting for “pure” computer scientists<br />They address an important set of “grand challenge” Computer Science issues including: <br />Heterogeneity<br />Scale<br />Scalability<br />Autonomic behaviour<br />Persistence, evolution<br />Deployment challenges<br />Mobility<br />Source: Dave de Roure<br />
    21. 21. A semanticperspectiveonthesechallenges<br />Sensor data querying and (pre-)processing<br />Data heterogeneity<br />Data quality<br />New inferencecapabilitiesrequiredtodealwith sensor information<br />Sensor data modelrepresentation and management<br />For data publication, integration and discovery<br />Bridgingbetween sensor data and ontologicalrepresentationsfor data integration<br />Ontologies: Observations and measurements, time series, etc.<br />Eventmodels<br />Userinteractionwith sensor data<br />
    22. 22. Vision (aftersomeiterations, and more to come)<br />12<br />RWI WorkingGrouponIoT: NetworkedKnowledgeGluhak et al, 2011. AnArchitecturalBlueprintfor a Real-World Internet', FutureInternet Assembly<br />
    23. 23. Semantic Sensor Web / LinkedStream-Sensor Data (LSD)<br />A representation of sensor/streamdata followingthestandards of LinkedData<br />ButwhatisLinked Data?<br />
    24. 24. WhatisLinked Data?<br />14<br /><ul><li>An extension of the current Web…
    25. 25. … where data are given well-defined and explicitly represented meaning, …
    26. 26. … so that it can be shared and used by humans and machines, ...
    27. 27. ... better enabling them to work in cooperation
    28. 28. And clear principles on how to publish data</li></li></ul><li>The fourprinciples (Tim Berners Lee, 2006)<br />Use URIs as names for things <br />Use HTTP URIs so that people can look up those names. <br />When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) <br />Include links to other URIs, so that they can discover more things. <br />http://www.w3.org/DesignIssues/LinkedData.html<br />15<br />http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html<br />http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html<br />
    29. 29. Semantic Sensor Web / LinkedStream-Sensor Data (LSD)<br />A representation of sensor/streamdata followingthestandards of LinkedData<br />Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data source<br />Usingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections<br />Earlyreferences…<br />AmitSheth, CoryHenson, and SatyaSahoo, "Semantic Sensor Web," IEEE Internet Computing, July/August 2008, p. 78-83<br />Sequeda J, Corcho O. LinkedStream Data: A Position Paper. Proceedingsof the 2nd International WorkshoponSemantic Sensor Networks, SSN 09<br />Le-Phuoc D, Parreira JX, Hauswirth M. Challengesin LinkedStream Data Processing: A Position Paper. Proceedingsof the3rd International WorkshoponSemantic Sensor Networks, SSN 10<br />
    30. 30. Let’schecksomeexamples<br />Meteorological data in Spain: automaticweatherstations<br />http://aemet.linkeddata.es/<br />Paperunder open review at theSemantic Web Journal<br />http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data<br />Live sensors in Slovenia<br />http://sensors.ijs.si/<br />ChannelCoastalObservatory in Southern UK<br />http://webgis1.geodata.soton.ac.uk/flood.html<br />And some more from DERI Galway, Knoesis, CSIRO, etc.<br />17<br />
    31. 31. AEMET Linked Data<br />18<br />
    32. 32. JSI Sensors<br />19<br />
    33. 33. Coastal Channel Observatory and other sources<br />20<br />Sensors, Mappings and Queries<br />Work with Flood environmental sensor data.<br />SemSorGrid4Env project www.semsorgrid4env.eu.<br />
    34. 34. PART II<br />How to create, publish and consume Linked Stream Data<br />
    35. 35. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    36. 36. <ul><li>Several efforts since approx. 2005
    37. 37. State of the art on sensor network ontologies in the report below
    38. 38. In 2009, a W3C incubator group was started, which has just finished
    39. 39. Lots of good people there
    40. 40. Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
    41. 41. Ontology: http://purl.oclc.org/NET/ssnx/ssn
    42. 42. A good number of internal and external references to SSN Ontology
    43. 43. http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography
    44. 44. SSN Ontology paper submitted to Journal of Web Semantics</li></ul>SSN ontologies. History<br />
    45. 45. Deployment<br />System<br />OperatingRestriction<br />Process<br />Device<br />PlatformSite<br />Data<br />Skeleton<br />ConstraintBlock<br />MeasuringCapability<br />Overview of the SSN ontology modules<br />
    46. 46. deploymentProcesPart only<br />Deployment<br />System<br />OperatingRestriction<br />hasSubsystem only, some<br />hasSurvivalRange only<br />SurvivalRange<br />DeploymentRelatedProcess<br />hasDeployment only<br />System<br />OperatingRange<br />Deployment<br />hasOperatingRange only<br />deployedSystem only<br />deployedOnPlatform only<br />Process<br />hasInput only<br />inDeployment only<br />Device<br />Input<br />Device<br />Process<br />onPlatform only<br />PlatformSite<br />Output<br />Platform<br />hasOutput only, some<br />attachedSystem only<br />Data<br />Skeleton<br />implements some<br />isProducedBy some<br />Sensor<br />Sensing<br />hasValue some<br />SensorOutput<br />sensingMethodUsed only<br />detects only<br />SensingDevice<br />observes only<br />SensorInput<br />ObservationValue<br />isProxyFor only<br />Property<br />isPropertyOf some<br />includesEvent some<br />observedProperty only<br />observationResult only<br />hasProperty only, some<br />observedBy only<br />Observation<br />FeatureOfInterest<br />featureOfInterest only<br />ConstraintBlock<br />MeasuringCapability<br />hasMeasurementCapability only<br />forProperty only<br />inCondition only<br />inCondition only<br />Condition<br />MeasurementCapability<br />Overview of the SSN ontologies<br />
    47. 47. SSN Ontology. Sensor and environmental properties<br />Skeleton<br />Property<br />Communication<br />MeasuringCapability<br />hasMeasurementProperty only<br />MeasurementCapability<br />MeasurementProperty<br />Accuracy<br />Frequency<br />Precision<br />Resolution<br />Selectivity<br />Latency<br />DetectionLimit<br />Drift<br />MeasurementRange<br />ResponseTime<br />Sensitivity<br />EnergyRestriction<br />OperatingRestriction<br />hasOperatingProperty only<br />OperatingProperty<br />OperatingRange<br />EnvironmentalOperatingProperty<br />MaintenanceSchedule<br />OperatingPowerRange<br />hasSurvivalProperty only<br />SurvivalRange<br />SurvivalProperty<br />EnvironmentalSurvivalProperty<br />SystemLifetime<br />BatteryLifetime<br />
    48. 48. A usageexample<br />Upper<br />SWEET<br />DOLCE UltraLite<br />SSG4Env <br />infrastructure<br />SSN<br />Schema<br />Service<br />External<br />OrdnanceSurvey<br />FOAF<br />Flood domain<br />CoastalDefences<br />AdditionalRegions<br />Role<br />27<br />
    49. 49. AEMET Ontology Network<br />83 classes<br />102 objectproperties<br />80 datatypeproperties<br />19 instances<br />SROIQ(D)<br />
    50. 50. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    51. 51. Goodpractices in URI Definition<br />Sorry, no clearpracticesyet…<br />
    52. 52. Goodpractices in URI Definition<br />Wehavetoidentify…<br />Sensors<br />Features of interest<br />Properties<br />Observations<br />Debate betweenbeingobservationor sensor-centric<br />Observation-centricseemsto be thewinner<br />Forsomedetails of sensor-centric, check [Sequeda and Corcho, 2009]<br />
    53. 53. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    54. 54. Queries to Sensor/Stream Data<br />SNEEql<br />RSTREAM SELECT id, speed, direction <br />FROM wind[NOW];<br />Streaming SPARQL<br />PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#><br />SELECT ?sensor ?speed ?direction<br />FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS<br />WHERE {<br /> ?sensor a fire:WindSensor;<br />fire:hasMeasurements ?WindSpeed, ?WindDirection.<br /> ?WindSpeed a fire:WindSpeedMeasurement;<br />fire:hasSpeedValue ?speed;<br />fire:hasTimestampValue ?wsTime.<br /> ?WindDirection a fire:WindDirectionMeasurement;<br />fire:hasDirectionValue ?direction;<br />fire:hasTimestampValue ?dirTime.<br /> FILTER (?wsTime == ?dirTime)<br />}<br />C-SPARQL<br />REGISTER QUERY WindSpeedAndDirection AS<br />PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#><br />SELECT ?sensor ?speed ?direction<br />FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]<br />WHERE { …<br />33<br />Semantically Integrating Streaming and Stored Data<br />
    55. 55. SPARQL-STR v1<br />34<br />Sensors, Mappings and Queries<br />SELECT ?waveheight<br />FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> <br />[FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE]<br />WHERE { <br /> ?WaveObs a sea:WaveHeightObservation;<br /> sea:hasValue ?waveheight; }<br />SELECT measuredFROM wavesamples [NOW -10 MIN]<br />conceptmap-def WaveHeightMeasurement<br />virtualStream <http://ssg4env.eu/Readings.srdf> <br />uri-as concat('ssg4env:WaveSM_',<br />wavesamples.sensorid,wavesamples.ts) <br />attributemap-defhasValue<br />operation constant <br />has-columnwavesamples.measured<br />dbrelationmap-def isProducedBy <br />toConcept Sensor <br />joins-via condition equals <br />has-column sensors.sensorid <br />has-columnwavesamples.sensorid<br />conceptmap-def Sensor <br />uri-as concat('ssg4env:Sensor_',sensors.sensorid) <br /> attributemap-def hasSensorid <br />operation constant <br />has-column sensors.sensorid<br />Query<br />translation<br />SNEEql<br />SPARQLStream<br />Query Processing<br />Stream-to-Ontology<br />mappings<br />Client<br />Sensor Network <br />Data<br />translation<br />[tuples]<br />[triples]<br />S2O Mappings<br />Source: EnablingOntology-based Access toStreaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010<br />
    56. 56. SPARQL-STR v2<br />SPARQLStream algebra(S1 S2 Sm) <br />GSN<br />Query<br />translation<br />q<br />SNEEql, GSN API<br />Sensor Network (S1)<br />SPARQLStream (Og) <br />Relational DB (S2)<br />Query Evaluator<br />Stream-to-Ontology<br />Mappings (R2RML)<br />Client<br />Stream Engine (S3)<br />RDF Store (Sm)<br />Data<br />translation<br />[tuples]<br />[triples]<br />Ontology-based Streaming Data Access Service<br />Source: PlanetDatadeliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu<br />
    57. 57. CreatingMappings<br />36<br />Sensors, Mappings and Queries<br />ssn:observedProperty<br />ssn:Observation<br />ssn:Property<br />http://swissex.ch/data#<br />Wan7/WindSpeed/Observation{timed}   <br />sweetSpeed:WindSpeed<br />ssn:observationResult<br />wan7<br />ssn:SensorOutput<br />timed: datetime PK<br />sp_wind: float<br />http://swissex.ch/data#<br />Wan7/ WindSpeed/ ObsOutput{timed}   <br />ssn:hasValue<br />ssn:ObservationValue<br />http://swissex.ch/data#<br />Wan7/WindSpeed/ObsValue{timed} <br />qudt:numericValue<br />xsd:decimal<br />sp_wind<br />
    58. 58. R2RML<br />RDB2RDF W3C Group, R2RML Mappinglanguage:<br />http://www.w3.org/2001/sw/rdb2rdf/r2rml/<br />37<br />Sensors, Mappings and Queries<br />:Wan4WindSpeed a rr:TriplesMapClass;<br />rr:tableName "wan7";<br />rr:subjectMap [ rr:template <br /> "http://swissex.ch/ns#WindSpeed/Wan7/{timed}";<br />rr:classssn:ObservationValue; rr:graphssg:swissexsnow.srdf ];<br />rr:predicateObjectMap [ rr:predicateMap [ rr:predicatessn:hasQuantityValue];<br />rr:objectMap[ rr:column "sp_wind" ] ]; . <br /><http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > <br />a ssn:ObservationValue<br /><http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > <br />ssn:hasQuantityValue "4.5"<br />
    59. 59. 38<br />Red de Ontologías para el Camino de Santiago<br />QueryTransformationSemantics<br /><ul><li>ConjunctiveQueries
    60. 60. Mapping</li></ul>expression<br />overstreamingsources<br />conjunctive<br />query<br />
    61. 61. Algebra expressions transformed to GSN API <br />39<br />Sensors, Mappings and Queries<br />π<br />SELECT sp_windFROM wan7 [NOW -5 HOUR] WHERE sp_wind >10<br />timed,<br />sp_wind<br />σ<br />sp_wind>10<br />ω<br />5 Hour<br />http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &<br />field [0]= sp_wind &<br />from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&<br />c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10<br />wan7<br />
    62. 62. Algebra construction<br />40<br />Sensors, Mappings and Queries<br />π<br />timed,<br />sp_wind<br />windsensor1<br />σ<br />windsensor2<br />sp_wind>10<br />ω<br />5 Hour<br />wan7<br />
    63. 63. Staticoptimization<br />41<br />Sensors, Mappings and Queries<br />π<br />π<br />π<br />timed,<br />sp_wind<br />timed,<br />windvalue<br />timed,<br />windvalue<br />σ<br />σ<br />σ<br />sp_wind>10<br />windvalue>10<br />windvalue>10<br />ω<br />ω<br />ω<br />5 Hour<br />5 Hour<br />5 Hour<br />wan7<br />windsensor1<br />windsensor2<br />
    64. 64. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    65. 65. Data Modeling: stRDF<br /><ul><li>stRDF
    66. 66. Temporal/spatial data are represented by linear constraints, representing as literals of type strdf:semiLinearPointSet.
    67. 67. OGC simple feature geometries (points, polylines, polygons etc.) using the Well-known Text representation</li></ul>floodInstances:ModelledFloodIngressDataset<br />rdf:typeinfo:Dataset; <br />rdfs:label "Modelled flood ingress Dataset" ; time:hasTemporalExtent "[NOW,NOW+12]"^^RegistryOntology:TemporalInterval;<br />Services:coversRegionAdditionalRegions:CoastalDefencePartnershipModelledArea; Services:includesFeatureTypeCoastalDefences:FloodPlain ; Services:includesPropertyTypeCoastalDefences:WaterDepth. <br />AdditionalRegions:CoastalDefencePartnershipModelledArea<br />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.<br />Source: Our NKUA partners at SemsorGrid4Env<br />2nd Year Review Meeting - Brussels, 16-17 Nov. 2010<br />43<br />
    68. 68. Querying: stSPARQL<br />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<br />select distinct ?ENDPOINT <br />where { ?SERVICE rdf:typeServices:WebService . <br /> ?SERVICE Services:hasEndpointReference ?ENDPOINT . <br /> ?SERVICE Services:hasServiceTypeServices:WMS . <br /> ?SERVICE Services:hasDataset ?DATASET .<br /> ?DATASET Services:includesFeatureTypeCoastalDefences:FloodPlain. <br /> ?DATASET time:hasTemporalExtent ?TIME . <br /> filter(?TIME contains “[NOW,NOW+12]"^^RegistryOntology:TemporalInterval) .<br /> ?DATASET Services:coversRegion ?SERVICEREGION .<br /> ?SERVICEREGION Services:hasSpatialExtent ?SERVICEREGIONGEO . <br />AdditionalRegions:CoastalDefencePartnershipModelledArea<br />Services:hasSpatialExtent ?COSTALGEO .<br /> filter(?SERVICEREGIONGEO contains ?COSTALGEO) }<br />Source: Our NKUA partners at SemsorGrid4Env<br />2nd Year Review Meeting - Brussels, 16-17 Nov. 2010<br />44<br />
    69. 69. Implementation: STRABON<br />SupportforstRDF and SPARQL, plus<br />Topologicaloperators in spatialfilters<br />DISJOINT, TOUCH, EQUALS, CONTAINS, COVERS, COVERED BY, OVERLAP<br />ConstructSpatialGeometries<br />e.g. ?geo1 union ?geo2<br />Projectionoperation<br />e.g. ?geo[1,2]<br />Renameoperator<br />ConversionFunctionsforexportinggeometries:<br />e.g. ToWKT(?geo) AS ?geoAsWKT<br />Library thatreturns SPARQL results as a KML document<br />45<br />Source: Our NKUA partners at SemsorGrid4Env<br />
    70. 70. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    71. 71. Sensor High-level API<br />Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env<br />
    72. 72. Sensor High-level API<br />Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env<br />
    73. 73. API definition<br />Source: Kevin Page and rest of Southampton’steam at SemsorGrid4Env<br />
    74. 74. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    75. 75. SwissEx<br />51<br />Sensors, Mappings and Queries<br />Global Sensor Networks, deployment for SwissEx.<br />Distributedenvironment: GSN Davos, GSN Zurich, etc.<br />In each site, a number of sensorsavailable<br />Each one withdifferentschema<br />Metadatastored in wiki<br />Federatedmetadata management:<br />Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T., Lehning, M.EffectiveMetadata Management in federatedSensor Networks.  in SUTC, 2010<br />Sensor observations<br />Sensormetadata<br />
    76. 76. Gettingthingsdone<br />Transformed wiki metadata to SSN instances in RDF<br />Generated R2RML mappings for all sensors<br />Implementation of Ontology-basedquerying over GSN<br />Fronting GSN with SPARQL-Stream queries<br />Numbers:<br />28 Deployments<br />Aprox. 50 sensors in eachdeployment<br />More than 1500 sensors<br />Live updates. Lowfrequency<br />Access to all metadata/not all data<br />52<br />Sensors, Mappings and Queries<br />
    77. 77. SensorMetadata<br />53<br />Sensors, Mappings and Queries<br />station<br />location<br />sensors<br />model<br />properties<br />
    78. 78. Sensor Data: Observations<br />54<br />Sensors, Mappings and Queries<br />Heterogeneity<br />Integration<br />
    79. 79. SPARQL-STR + GSN<br />
    80. 80. HowtodealwithLinkedStream/Sensor Data<br />Ingredients<br />Anontologymodel<br />Goodpractices in URI definition<br />Supportingsemantictechnology<br />SPARQL extensions<br />Tohandle time and tuplewindows<br />Tohandlespatio-temporal constraints<br />REST APIstoaccessit<br />Anotherexample: semanticallyenriching GSN<br />A couple of lessonslearned<br />
    81. 81. LessonsLearned<br />High-level (part I)<br />Sensor data isyetanothergoodsource of data withsomespecialproperties<br />Everythingthatwe do withourrelationaldatasetsorother data sources can be done with sensor data<br />Practicallessonslearned (part II)<br />Manageseparatelydata and metadata of thesensors<br />Data shouldalways be separatedbetweenrealtime-data and historical-data<br />Use the time formatxsd:dateTimeand the time zone<br />Graphicalrepresentation of data forweeksormonthsisnot trivial anyway<br />
    82. 82. Ingredients for the Semantic Sensor Web<br />Jožef Stefan Institute<br />Ljubljana, Slovenia<br />September 23rd 2011<br />Oscar Corcho<br />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) <br />

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