4th Future Internet Symposium FIS 2011                   Vienna, AustriaLinked Sensor Data 101      Oscar Corcho, Jean-Pau...
Linked Sensor Data 101Motivation                 Ingredients     Linked Sensor Data                        GenerateConsume...
MotivationFrom Sensor Networks…      … to the Sensor Web/               Internet of Things…                 … to Semantic ...
Sensors                                     (t9, a1,   a2, ... , an)                                     (t8, a1,   a2, .....
Sensor NetworksSource: Antonis Deligiannakis
An example: SmartCitiesEnvironmental sensors     Parking sensors 6             SmartSantander Project
Who are the end users of Sensor Networks?The climate change expert, or a simple citizenSource: Dave de Roure
Not only environmental, but many others…Weather Sensors                                                         GPS Sensor...
The Sensor Web       Universal, web-based access to sensor dataSource: Adapted from Alan Smeaton’s invited talk 9 ESWC2009...
Make sensors more accessible?Source: SemsorGrid4Env consortium    10
Should we care as computer scientists?      “Grand Challenge” CS issues:      • Heterogeneity      • Scale      • Scalabil...
Vision (after some iterations, and more to come)Networked               Before 2010                2010-2015              ...
Semantic Sensor Web / Linked Sensor Data (LSD)A representation of sensor data followingthe standards of Linked Data       ...
What is Linked Data?An extension of the current Web…  data are given well defined  and explicitly represented meaning     ...
The four principles (Tim Berners Lee, 2006)Use URIs as names of thingsUse HTTP URIsProvide useful information when URI is ...
Semantic Sensor Web / Linked Sensor Data (LSD)    A representation of sensor data following    the standards of Linked Dat...
Let’s check some examples• Meteorological data in Spain: automatic weather  stations   • http://aemet.linkeddata.es/• Live...
AEMET Linked Data          Sensors          Observations18
JSI Sensors19
Coastal Channel Observatory and other sources• Work with Flood environmental sensor data.• SemSorGrid4Env project www.sems...
Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifie...
Sensor Network Ontologies Since aprox. 2005: Several proposals      Project specific      Reuse?      Alignment?     ...
SSN ontology modules                                                        System               OperatingRestriction     ...
Overview of the SSN ontologiesDeployment                             deploymentProcesPart only        System              ...
SSN Ontology: Measurement CapabilitiesSkeleton  Property           MeasuringCapability                                    ...
Exampleswissex:Sensor1    rdf:type ssn:Sensor;    ssn:onPlatform swissex:Station1;    ssn:observes [rdf:type sweetSpeed:Wi...
Exampleswissex:WindSpeedObservation1    rdf:type ssn:Observation;    ssn:featureOfInterest [rdf:type sweetAtmoWind:Wind]; ...
Usage: SSN & Domain OntologiesUpper                         DOLCE                           SWEET                         ...
AEMET Ontology Network•   83 classes•   102 object properties•   80 datatype properties•   19 instances                   ...
Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifie...
Good practices in URI DefinitionSorry, no clearpractices yet…
Good practices in URI Definition • URIs for:    •   Observations    •   Sensors    •   Features of interest    •   Propert...
Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifie...
Sensor High-level APISource: K. Page & Southampton’s team at SemsorGrid4Env
Sensor High-level APISource: K. Page & Southampton’s team at SemsorGrid4Env
Queries to Sensor DataSNEEqlRSTREAM SELECT id, speed, direction FROM wind [NOW];Streaming SPARQLPREFIX fire: <http://www.s...
GSN & Swiss-Experiment• Global Sensor Networks, deployment for SwissEx.• Distributed environment: GSN Davos, GSN Zurich,  ...
Where is the Data?GSN server instance                       ..                       wan7                       sensor1   ...
Creating Mappings                                                                 ssn:observedProperty                    ...
Querying the Observations                        SELECT ?waveheight                        FROM STREAM <www.ssg4env.eu/Sen...
ConclusionsIngredients for Linked Sensor Data  Core ontology  Domain ontologies  Guidelines for identifiers  APIs  Query p...
Thanks!Acknowledgments: all those identified in slides + the SemsorGrid4Env team (AlasdairGray, Kevin Page, etc.), the AEM...
Where is the Data?GSN server instance                       ..                       wan7                       sensor1   ...
Creating Mappings                                                                 ssn:observedProperty                    ...
R2RML• RDB2RDF W3C Group, R2RML Mapping language:     • http://www.w3.org/2001/sw/rdb2rdf/r2rml/  :Wan4WindSpeed a rr:Trip...
Data Access• GSN Web Services• GSN URL API     • Compose the query as a URL:        http://montblanc.slf.ch :22001/ multid...
Using the Mappings                                                                             π timed,                   ...
Algebra expressionsπ timed,              http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &                      fi...
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Linked Sensor Data 101 (FIS2011)

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  • Addingsemanticsallowsthesearch and exploration of sensor data withoutany prior knowledge of the data sourceUsingtheprinciples of Linked Data facilitatestheintegration of stream data totheincreasingnumber of Linked Data collections
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • The where clasue for both SPARQL extensions is the same
  • - A core ontological model that can be used to describe sensor data streams, including the metadata about the sensor data sources and their observations. We take into account here the ontology developed for this purpose in the context of the W3C Semantic Sensor Network Incubator Group, which can be considered the current standard to be followed. - A set of additional domain ontologies in the area in which sensors are applied and for which they generate measurements (e.g., if we deal with environmental sensors related to water, then ontologies about different aspects of water will be needed). These ontologies must be aligned with the previous core ontology.Guidelines for the generation of identifiers (in the form of URIs, since this is one of the key ingredients of Linked Data) for sensors, their observations and the features of interest that they observe.Supporting Sensor Web programming interfaces (APIs) that make use of the HTTP protocol for serving the corresponding data whenever the previous URIs are dereferenced. Query processing engines that support extended versions of SPARQL (the query language used for Linked Data) and handle some of the most characteristic aspects of data streams, such as time and/or tuple windows. The management of spatio-temporal extensions of this query language may be also useful in this context.
  • Linked Sensor Data 101 (FIS2011)

    1. 1. 4th Future Internet Symposium FIS 2011 Vienna, AustriaLinked Sensor Data 101 Oscar Corcho, Jean-Paul Calbimonte, Raúl García-Castro and Freddy Priyatna Ontology Engineering Group. Facultad de Informática, Universidad Politécnica de Madrid. jp.calbimonte@upm.es Date: 09/11/2011
    2. 2. Linked Sensor Data 101Motivation Ingredients Linked Sensor Data GenerateConsume 2
    3. 3. MotivationFrom Sensor Networks… … to the Sensor Web/ Internet of Things… … to Semantic Sensor Web and … Linked Sensor Data 3
    4. 4. Sensors (t9, a1, a2, ... , an) (t8, a1, a2, ... , an) Streaming (t7, a1, a2, ... , an)• Cheaper Data ... ...• Ubiquitous (t1, a1, ... a2, ... , an)• Robust ...• Routing • Noisy • Processing • Memory • Energy (Limited) http://www.flickr.com/photos/wouterh/2409251427/ 4
    5. 5. Sensor NetworksSource: Antonis Deligiannakis
    6. 6. An example: SmartCitiesEnvironmental sensors Parking sensors 6 SmartSantander Project
    7. 7. Who are the end users of Sensor Networks?The climate change expert, or a simple citizenSource: Dave de Roure
    8. 8. Not only environmental, but many others…Weather Sensors GPS Sensors Sensor Dataset Satellite Sensors Camera SensorsSource: H Patni, C Henson, A Sheth 8
    9. 9. The Sensor Web Universal, web-based access to sensor dataSource: Adapted from Alan Smeaton’s invited talk 9 ESWC2009 at
    10. 10. Make sensors more accessible?Source: SemsorGrid4Env consortium 10
    11. 11. Should we care as computer scientists? “Grand Challenge” CS issues: • Heterogeneity • Scale • Scalability • Autonomic behaviour • Persistence, evolution • Deployment challenges • Mobility Anything left for Semantic Web research?Source: Dave de Roure
    12. 12. Vision (after some iterations, and more to come)Networked Before 2010 2010-2015 2015-2020 Beyond 2020Knowledge Today Incremental Incremental- Visionary VisionaryInteroperability  Middleware  Intra-network cross-  Inter-network  Sensor ontologies layer integration and cross-layer optimization integration and  Sensor Internet optimizationInformation &  Relational  Stream aggregation  Database-stream  QoS-basedContext database  Query processing and integration information integration reasoning on sensor  Sensor actuation integration of  Sensor network networks (In-network DB and streams data warehouses  Event modelling processing)  QoS modelsDiscovery  Centralised non-  Semantic discovery of semantic registries sensors and sensor (sensorbase.org) data  Distributed registries  Sensor network location transparencyIdentity & Trust  RFID tags  URIs  Virtual sensor& Privacy  No privacy mgmnt  User-centric privacy and networks through policies dynamic policiesProvenance  Data provenance  Data transformation  Process and  Problem solving (where, what and processes (how) problem solving interpretation who) understanding and explanationRWI Working Group on IoT: Networked Knowledge (why) 12
    13. 13. Semantic Sensor Web / Linked Sensor Data (LSD)A representation of sensor data followingthe standards of Linked Data But what is Linked Data?
    14. 14. What is Linked Data?An extension of the current Web… data are given well defined and explicitly represented meaning So that it can be shared and used By humans and machines And clear principles on how to publish data 14
    15. 15. The four principles (Tim Berners Lee, 2006)Use URIs as names of thingsUse HTTP URIsProvide useful information when URI is dereferencedLink to other URIs http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html 15
    16. 16. Semantic Sensor Web / Linked Sensor Data (LSD) A representation of sensor data following the standards of Linked Data• Early references… • Sheth A, Henson C, and Sahoo S, Semantic Sensor Web, IEEE Internet Computing, 2008. • Sequeda J, Corcho O. Linked Stream Data: A Position Paper. Proceedings of the 2nd International Workshop on Semantic Sensor Networks, 2009. • Le-Phuoc D, Parreira JX, Hauswirth M. Challenges in Linked Stream Data Processing: A Position Paper. Proceedings of the 3rd International Workshop on Semantic Sensor Networks, 2010.
    17. 17. Let’s check some examples• Meteorological data in Spain: automatic weather stations • http://aemet.linkeddata.es/• Live sensors in Slovenia • http://sensors.ijs.si/• Channel Coastal Observatory in Southern UK • http://webgis1.geodata.soton.ac.uk/flood.html• And some more from DERI Galway, Knoesis, CSIRO, etc. 17
    18. 18. AEMET Linked Data Sensors Observations18
    19. 19. JSI Sensors19
    20. 20. Coastal Channel Observatory and other sources• Work with Flood environmental sensor data.• SemSorGrid4Env project www.semsorgrid4env.eu. Wind Speed Wave Height Tidal Observations 20
    21. 21. Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines http://www.flickr.com/photos/santos/2252824606/
    22. 22. Sensor Network Ontologies Since aprox. 2005: Several proposals  Project specific  Reuse?  Alignment?  Best practices? 2009-2011: W3C SSN-XG incubator group  SSN Ontology: http://purl.oclc.org/NET/ssnx/ssn
    23. 23. SSN ontology modules System OperatingRestriction Deployment Device Process PlatformSiteData Skeleton MeasuringCapability ConstraintBlock
    24. 24. Overview of the SSN ontologiesDeployment deploymentProcesPart only System OperatingRestriction hasSubsystem only, some hasSurvivalRange only SurvivalRange DeploymentRelatedProcess hasDeployment only System OperatingRange Deployment deployedSystem only hasOperatingRange only deployedOnPlatform only Process inDeployment only Device hasInput only InputPlatformSite onPlatform only Device Process Platform Output attachedSystem only hasOutput only, someData Skeleton isProducedBy some implements some Sensor Sensing hasValue some sensingMethodUsed only SensorOutput detects only SensingDevice observes only ObservationValue SensorInput isProxyFor only Property includesEvent some isPropertyOf some observedProperty only observationResult only observedBy only hasProperty only, some Observation FeatureOfInterest featureOfInterest only MeasuringCapability ConstraintBlock hasMeasurementCapability only forProperty only inCondition only inCondition only MeasurementCapability Condition
    25. 25. SSN Ontology: Measurement CapabilitiesSkeleton Property MeasuringCapability Communication hasMeasurementProperty only MeasurementCapability MeasurementProperty Accuracy Resolution Selectivity Frequency Precision Latency DetectionLimit Drift ResponseTime Sensitivity MeasurementRange OperatingRestriction EnergyRestriction Core ontological model hasOperatingProperty only OperatingRange OperatingProperty EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange hasSurvivalProperty only SurvivalRange SurvivalProperty EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime
    26. 26. Exampleswissex:Sensor1 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetSpeed:WindSpeed].swissex:Sensor2 station rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetTemp:Temperature].swissex:Station1 :hasGeometry [ rdf:type wgs84:Point; wgs84:lat "46.8037166"; wgs84:long "9.7780305"]. 26
    27. 27. Exampleswissex:WindSpeedObservation1 rdf:type ssn:Observation; ssn:featureOfInterest [rdf:type sweetAtmoWind:Wind]; ssn:observedProperty [rdf:type sweetSpeed:WindSpeed]; ssn:observationResult [rdf:type ssn:SensorOutput; ssn:hasValue [qudt:numericValue "6.245"^^xsd:double]]; ssn:observationResultTime [time:inXSDDatatime "2011-10-26T21:32:52"]; ssn:observedBy swissex:Sensor1 ; WindSpeed : 6.245 At: 2011-10- 26T21:32:52 27
    28. 28. Usage: SSN & Domain OntologiesUpper DOLCE SWEET UltraLiteSSG4Envinfrastructure SSN Schema ServiceExternal FOAF Ordnance SurveyFlood domain Role Coastal Additional Defences Regions 28
    29. 29. AEMET Ontology Network• 83 classes• 102 object properties• 80 datatype properties• 19 instances Additional domain ontologies
    30. 30. Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines http://www.flickr.com/photos/santos/2252824606/
    31. 31. Good practices in URI DefinitionSorry, no clearpractices yet…
    32. 32. Good practices in URI Definition • URIs for: • Observations • Sensors • Features of interest • Properties • Time periods • Debate: observation or sensor-centric? • Observation-centric seems to be the winner • Sensor-centric, check [Sequeda and Corcho, 2009] • Example:http://aemet.linkeddata.es/resource/Observation/at_1316382600000_of_08130_on_VV10m when sensor property
    33. 33. Ingredients for Linked Sensor DataCore ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines http://www.flickr.com/photos/santos/2252824606/
    34. 34. Sensor High-level APISource: K. Page & Southampton’s team at SemsorGrid4Env
    35. 35. Sensor High-level APISource: K. Page & Southampton’s team at SemsorGrid4Env
    36. 36. Queries to Sensor DataSNEEqlRSTREAM SELECT id, speed, direction FROM wind [NOW];Streaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?WindSpeedFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?sensor fire:hasMeasurements ?WindSpeed FILTER (?WindSpeed<30)}C-SPARQLREGISTER QUERY WindSpeedAndDirection ASPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]WHERE { … 36
    37. 37. GSN & Swiss-Experiment• Global Sensor Networks, deployment for SwissEx.• Distributed environment: GSN Davos, GSN Zurich, etc. • In each site, a number of sensors available • Each one with different Sensor observations schema• Metadata stored in wiki Sensor metadata 37
    38. 38. Where is the Data?GSN server instance .. wan7 sensor1 sensor2 timed: datetime PK GSN sensor3 sp_wind: float … Mappings ssn:Observation 38
    39. 39. Creating Mappings ssn:observedProperty ssn:Observation ssn:Property http://swissex.ch/data# ssn:observationResult Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed wan7 ssn:SensorOutputtimed: datetime PK http://swissex.ch/data#sp_wind: float ssn:hasValue Wan7/ WindSpeed/ ObsOutput{timed} ssn:ObservationValue http://swissex.ch/data# qudt:numericValue Wan7/WindSpeed/ObsValue{timed} xsd:decimal sp_wind 39
    40. 40. Querying the Observations SELECT ?waveheight FROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW -10 MINUTES TO NOW STEP 1 MINUTE] WHERE { ?WaveObs a sea:WaveHeightObservation; :22001/ multidata ?vs [0]= wan7 & http://montblanc.slf.ch field [0]= sp_wind sea:hasValue ?waveheight; } Query:Wan4WindSpeed a rr:TriplesMapClass; translation GSN rr:tableName "wan7"; SPARQLStream API rr:subjectMap [ rr:template"http://swissex.ch/ns#WindSpeed/Wan7/{timed}";Client rr:class ssn:ObservationValue; rr:graphssg:swissexsnow.srdf ]; Mappings Query rr:predicateObjectMap [ rr:predicateMap [ Processingrr:predicate ssn:hasQuantityValue ]; Sensor rr:objectMap[ rr:column "sp_wind" ] ]; Network [tuples] Data [triples] translation R2RML Mappings Query processing engines 40
    41. 41. ConclusionsIngredients for Linked Sensor Data Core ontology Domain ontologies Guidelines for identifiers APIs Query processing enginesWork in progress & examplesChallenges: generate & consume LSD
    42. 42. Thanks!Acknowledgments: all those identified in slides + the SemsorGrid4Env team (AlasdairGray, Kevin Page, etc.), the AEMET team at OEG-UPM (Ghislain Atemezing, Daniel Garijo,José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET) Questions, please. jp.calbimonte@upm.es 42
    43. 43. Where is the Data?GSN server instance .. wan7 sensor1 sensor2 timed: datetime PK GSN sensor3 sp_wind: float … Mappings ssn:Observation 43
    44. 44. Creating Mappings ssn:observedProperty ssn:Observation ssn:Property http://swissex.ch/data# ssn:observationResult Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed wan7 ssn:SensorOutputtimed: datetime PK http://swissex.ch/data#sp_wind: float ssn:hasValue Wan7/ WindSpeed/ ObsOutput{timed} ssn:ObservationValue http://swissex.ch/data# qudt:numericValue Wan7/WindSpeed/ObsValue{timed} xsd:decimal sp_wind 44
    45. 45. R2RML• RDB2RDF W3C Group, R2RML Mapping language: • http://www.w3.org/2001/sw/rdb2rdf/r2rml/ :Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate ssn: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" 45
    46. 46. Data Access• GSN Web Services• GSN URL API • Compose the query as a URL: 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_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 ? SPARQL-StreamCalbimonte, J-P., Corcho O., Gray, A. Enabling Ontology-based Access to Streaming Data Sources. In ISWC 2010. 46
    47. 47. Using the Mappings π timed, sp_windSELECT ?waveheight σFROM STREAM <www.ssg4env.eu/SensorReadings.srdf>[NOW – 5 HOUR TO NOW] sp_wind>10WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; ω 5 Hour FILTER (?waveheight>10) } wan7 wan7 ssn:ObservationValue http://swissex.ch/data# timed: datetime PK qudt:numericalValue Wan7/WindSpeed/ObsValue{timed} sp_wind: float xsd:datatype sp_wind 47
    48. 48. Algebra expressionsπ timed, 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& sp_wind c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10σ sp_wind>10ω 5 Hour SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10wan7 48

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